Paper_ Off-Line Signature Verification Based on Geometric Feature Extraction and Neural Network Classification

8/3/2019 Paper_ Off-Line Signature Verification Based on Geometric Feature Extraction and Neural Network Classification

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Pergamon Pattern Recognition, Vol. 30, No. I, pp. 9-17, 1997Copyright 1996 Pattern Recognition Society. Published by Elsevier ScienceLt dPrinted in Great Britain. Al l rights reserved0031-3203/97 $17.00+.00

PII:S0031-3203(96)00063-5O F F -L I N E S I G N A T U R E V E R I FI C A TI O N B A S E D O N

G E O M E T R IC F E A TU R E E X T R A C T I O N A N DN E U R A L N E T W O R K C L A SS IF IC A T IO N

K A I H U A N G * a nd H O N G Y A NDepartment of Electrical Engineering, U niversity o f Sydney, NSW 2006, Australia

(Rece ived 25 October 1995; in rev i sed form 26 M a r c h 1996; rece ived for publ ica t ion 15 A p r i l 1996)Abstract--In this paper a method for off-line signature verification based on geom etric feature extraction andneural network classification is proposed. The role of signature shape description and shape similarity measureis discussed in the context of signature recognition and verification. G eometric features o f input signature imageare simultaneously examined under several scales by a neural network classifier. An overall match rating isgenerated by combining the outputs at each scale. Artificially generated genuine and forgery samples fromenrollment reference signatures are used to train the network, which allows definite training control and at thesame tim e significantly reduces the number o f enrollment samples required to achieve a good performance.Experiments show that 90% correct classification rate can be achieved o n a database of over 3000 signatureimages. Copyright 19 96 Pattern Recognition Society. Published by Elsevier Science Ltd.Off-line signature verificationSignature image alignment Signature shape description and similarity measureNeural network classifier

1. I N T R O D U C T I O NB i o m e t r i c v e r i f i c a t i o n i s a n i m p o r t a n t r e s e a r c h a r e at a r g e t e d a t a u t o m a t i c i d e n t i t y v e r i f i c a t i o n a p p l i c a t i o n s .C u r r e n t s e c u r i ty p r a c t i c e s u s u a l l y i n v o l v e t h e u s e o f P I Nn u m b e r s , p a s s w o r d s , a n d a c c e s s c a rd s . T h e s e t o k e n s a r en o t v e r y r e l i a b l e i n t h a t t h e y c a n b e f o r g o t t e n o r l o s t, a n dn o f u r t h e r r e s t r i c t i o n s e x i s t w h i c h c a n p r e v e n t a nu n a u t h o r i z e d p e r s o n f r o m u s i n g t h e m i n a n a u t o m a t i cm a c h i n e v e r i f i c a t i o n e n v i r o n m e n t . B i o m e t r i c m e a s u r e s ,o n t h e o t h e r h a n d , a r e n o t e a s i l y d u p l i c a t e d a n d c a n n o tb e l o s t o r s t o l e n , h e n c e a r e m o r e s e c u r e . P o w e r f u lc o m p u t e r t e c h n o l o g i e s t o d a y c a n e a s i l y a c c o m m o d a t ec o m m o n b i o m e t r i c v e r i f i c a t io n ta s k s, e s p e c i a l l y w i t h t h ei n t r o d u c t i o n o f s m a r t c a r d s.

T h e r e a r e t w o t y p e s o f b i o m e t r i c s : p h y s i o l o g i c a l , e .g .i r i s p a t t e r n a n d f in g e r p r in t ; b e h a v io r a l , e . g . s p e e c h a n dh a n d w r i t i n g . H a n d w r i t t e n s i g n a t u re v e r i f i c a t i o n is ab e h a v i o r a l b i o m e t r i c v e r i f i c a t i o n , a n d m o s t o f u s a r ef a m i l i a r w i t h t h e p r o c e s s o f v e r i f y i n g a h a n d w r i t i n ga g a in s t a s ig n a tu r e r e c o r d f o r id e n t i f i c a t io n , e s p e c ia l lyi n l e g a l , b a n k i n g , a n d o t h e r h i g h s e c u r i ty e n v i r o n m e n t s .

A u t o m a t i c h a n d w r i t t e n s i g n a t u r e v e r if i c a t i o n s y s t e m s( A H S V S ) a r e e i t h e r o n - l i n e o r o f f - l i n e , w h i c h a r ed i f f e r e n t i a t e d b y th e d a ta a c q u i s i t io n m e th o d . ( ~- 3) I n a no n - l in e s y s te m, s ig n a tu r e t r a c e s a r e a c q u i r e d in r e a l t imew i t h d i g i t i z i n g t a b l e t s , i n s t r u m e n t e d p e n s , o r o t h e rs p e c i a l i z e d h a r d w a r e s d u r i n g t h e s i g n i n g p r o c e s s. I n a no f f - l in e s y s t e m , s i g n a t u r e i m a g e s a r e a c q u i r e d w i t hs c a n n e r s o r c a m e r a s a f t e r t h e c o m p l e t e s i g n a t u r e s h a v e* Author to wh om correspondence should be addressed. Tel.:+61 2 351-4824; fax: +61 2 351-3847; e-mail: [email protected].

b e e n w r i t t e n . C o m m o n t o b o t h t y p e s o f s y s t e m s a r e t h es t a t i c c a l l i g r a p h i c i n f o r m a t i o n , i . e . t h e g e o m e t r i cp r o p e r t ie s o f th e s i g n a t u re . D y n a m i c h a n d w r i t i n gf e a tu r e s , s u c h a s mo t io n , p r e s s u r e , a n d t imin g in f o r ma -t io n , a r e r e a d i ly c a p tu r e d o n - l in e , b u t n o t r e c o v e r a b lewi th g o o d a c c u r a c y o f f - l in e . (1 )

T h e c o m p l e x i t y o f s i g n a t u r e v e r i f i c a t i o n l i e s i n t h ev a r i a b i l i ty o f s ig n in g . F o r we l l - p r a c t i c e d s ig n a tu r e s ,i n t e r p e r s o n a l v a r i a t i o n s s h o u l d b e m u c h l a r g e r t h a ni n t r ap e r s o n a l o n e s . T h e a i m o f a n a u t o m a t i c s i g n a t u rev e r i f i c a t io n s y s te m i s to e x t r a c t a s e t o f l e a s t v a r i a b lef e a tu r e s f r o m r e f e r e n c e s ig n a tu r e s a mp le s , s u c h th a t th ei n t r a p e r s o n a l v a r i a t i o n s a r e m i n i m i z e d . T h e s e f e a t u r e sa r e t h e n u s e d t o f o r m a s i m i l a r i t y m e a s u r e t o c l a s s i f yf u r th e r in p u t s a s e i th e r g e n u in e o r f o r g e r y , b a s e d o n th ec l o s e n e s s o f t h e m a t c h .

M o s t o f A H S V S a r e o n - l i n e s y s t e m s w h i c h u t i l i z ed y n a m i c f e a t u r e s t o a c h i e v e e x c e l l e n t v e r i f i c a t i o nr e s u l t s . (1 '4 ) A wid e r a n g e o f t e c h n iq u e s h a v e b e e na p p l i e d i n t h e p a s t t o s o l v e t h e m o r e d i f f i c u l t o f f - l i n es i g n a t ur e v e r i f i c a t i o n p r o b l e m s . E l a s t i c i m a g e m a t c h i n gh a s b e e n s u c c e s s f u l l y u s e d b y d e B r u y n e e t a l . t o r e d u c er a n d o m f o r g e r y a c c e p ta n c e r a t e . (5 ) E la s t i c s ig n a tu r ee n v e l o p m a t c h i n g h a s a l s o b e e n u s e d t o c o m p u t ed i s t a n c e s b e tw e e n s ig n a tu r e s . (6 ) T e c h n iq u e s th a t a r et r a d i t i o n a l l y d e v e l o p e d f o r c h a r a c t e r r e c o g n i t i o n a r ea l s o b e i n g a p p l i e d t o s i g n a t u r e v e r i f i c a t i o n . F o re x a m p l e , e x t e n d e d s h a d o w - c o d e h a s b e e n u s e d a s ag l o b a l s i g n a t u r e s h a p e d e s c r i p t o r i n t h e e l i m i n a t i o n o fr a n d o m f o r g e r i e s . (7 )

E f f e c t iv e f e a tu r e e x t r a c t io n i s imp o r ta n t in a l l p a t t e r nr e c o g n i t i o n t a s k s . S i g n a t u r e i m a g e a n a l y s i s b a s e d o ng r a d ie n t o p e r a to r d e r iv e d f e a tu r e s s u c h a s d i r e c t io n a l


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Probability Density Funct ion (PDF) (8) has been inves-tigated, and image understanding based approach hasalso been implemented with good results on randomforgery data. (9) Analysis based on various histogramsand their derivatives has been reported by Ammar,which are successful in the detection of highly skilledforgeries. (1) More recent efforts tend to util ize neuralnetworks and multi -resolution image analysis in theclassification of signature data. (4)

The role of signature shape representation andsignature similarity measure derived from shape repre-sentation needs to be emphasized. (11-13) In this paper,we use the following features to describe the shape of asignature: core, outline, ink area distribution, andsignature frontiers. A set of directional filters are usedto segment the signature images using these featuresinto consistent parts. A statistical model for each signa-ture class can then be built from a few training referencesamples, and a similarity measure can be set to classifygenuine and forgery inputs with reasonable confidence.

The proposed method in this paper uses multi-resolution feature extraction and multiple expert votingtechniques. When examining handwritten signatures,people usually perform the following steps. First therough overall shape is compared. Then closer detailssuch as how each stroke starts and ends, the ink tracepath and other aspects of the writing are looked atindividually. Finally the overall impression of authen-ticity is made. The current study is aimed at the firststage signature classification, which performs roughshape comparison to filter out most of the less skilledforgery inputs, simulating the above steps. Signatureshape is learned by neural networks at multipleresolutions selected by experiments. A set of multi-resolution grids with fuzzified borders are overlaid ontop of signature shape representations when extractinglocal shape features. Neural network structure is used inthe construction of a statistical model at every gridresolution for each signature class. The classifier, whichis the trained neural network model, is also built.Artificial genuine and forgery samples are created totrain the neural network classifier by applying perturba-tion to a small set of genuine reference samples. Theacceptance and rejection space are effectively controlledby perturbation parameters.

The rest of the paper is divided into five sections. InSection 2, the signature database used in our experi-ments is described. In Section 3, signature shape featureextraction and alignment are discussed. Neural networkclassifier training method is described in Section 4. InSection 5, system implementat ion and experimentresults are discussed. Finally, conclusions of the studyare presented in Section 6.

2 . S I G N A T U R E D A T A B AS E C O N S T R U C T I O N2.1. Signature data collection

A total of 3528 signature images are collected to formthe signature database. These images belong to 21 sets

of different signatures. Each set comprises one A4 pageof genuine and six A4 pages of forgery signatures. Theyare scanned, one page at a time, at resolution of 100 dpi,8-bit gray-scale. An A4 page is divided into 122rectangles with dashed lines. Twenty-four genuinesignatures are signed by a volunteer and six lots of 24forged signatures are produced by other volunteers.Each volunteer may be asked to produce one to threeother people's signatures, given photocopies of thegenuine signature pages. The forgeries, either freehandor traced, encompass varying skill levels (Fig. 1).2.2. Signature image preprocessing

The signature images are first cut out from thescanned A4 page image by a separate form processingprogram. The dash l ines on the form are located and areused as the primary separators in the extraction ofindividual images. It is observed that some writers usethe lower grid lines as their signature reference line, as aresult part of the signature trace is cut off by theextraction program. From visual inspection, the ex-tracted images contain most of the signature informationand some loss is tolerable. It helps to force theclassification system to be more robust against suchsituations.

Excessive white area in each image is trimmed off,and the signature is centered at gray-level centroid byadding clean border areas to make it more pleasing tothe eye. Standard noise reduction and isolated peaknoise removal techniques, such as median-filte ring andaverage filtering, 15) are used to clean the init ial image.A binarized signature mask is obtained by thresholdingfollowed by morphological operations 15) to fill smallholes and to remove small connected componentsmostly generated by noisy background. It is used tomask out the gray-leveled version of the clean, centeredsignature image S g r a y (Fig. 2).

3. EXTRACTIONAND ALIGNMENTOFS I G N A T U R E S H A P EF E A T U R E S

3.1. Feature extractionFor a signature image Sg~ay of width w and height h,

let P be a pixel inside Sgray. The position of P is denotedby (i, j), where O


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( d )F i g . 1 . E x a m p l e s o f g e n u i n e a n d f o r g e r y s i g n a t u r e s. G e n u i n e s a m p l e s a r e o n t h e l ef t , a n d f o r g e r y s a m p l e sa r e o n t h e r i g h t . T h e f o r g e d s i g n a t u r e s a re e i t h e r s i m p l e f r e e h a n d , s k i l l e d f r e e h a n d o r t r a c e d . ( a ) a n d ( b ) s h o wc o m m o n c u r s i v e ty p e s i g n a t u r e s a m p l e s a n d f o r g e r i e s , ( c ) s h o w s g r a p h i c a l t y p e s i gn a t u r e s , a n d ( d ) s h o w sor ien ta l type s igna tu res .


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12 K. HUAN G and H. YAN

( b )LAd,.

( c )Fig. 2. Preprocessing of signature image. (a) is the signature cut-out from form, (b) is the binarized mask,and (c) is the cleaned image after processing.

Co re fea ture F~ore :S ig n a tu re c o re i s d e f in e d a s t h e sk e l e to n o f t h e p e nt r a c e . Th e c o re f e a tu re i s a u se fu l s t ru c tu ra l r e p re se n t a -t i o n o f a s i g n a tu re . In c o n ju n c t io n wi th o th e r f e a tu re s , i ti s p o s s i b l e t o e x t r a ct d o m i n a n t a n d i n d i v i d u a l p e ns t ro k es . I t i s a l so i n v a r i a n t wi th r e sp e c t t o p e n t r a c eth i c k n e ss . S t r i c t s i n g l e p ix e l c o n n e c t iv i t y i s n o t c r i t i c a li n o u r c u r r e n t a p p l i c a ti o n . I n s t e a d o f a p p l y i n g s t a n d a r ds k e l e t o n i z in g a l g o r i th m o n b i n a r i z e d i m a g e , t h e c o r efe a tu re i s e x t r a c t e d d i r e c t l y f ro m th e g ra y - l e v e l im a g eu s in g a 3 3 o p e ra to r , t o p re se rv e t h e o r ig in a l s t ru c tu rea s c lo se ly a s p o ss ib l e . A p ix e l i s c o re i f it s g ra y - l e v e lv a lu e i s a l o c a l p e a k (F ig . 3 a ) :

7P E F . . . . . i f Z s t e p ( g e >- g ek ) < 6 ,

k = 0w h e r e

> g P k ) = [ 1 , . , i f gp >_ gek, ( 1 )s t ep (g p - ( v o therw ise . S ig n a tu re o u t l i n e F o u t l i n e :

T h e o u t l i n e f e a t u r e c o n t a i n s m o s t o f t h e s h a p ein fo rm a t io n . I t i s e x t r a c t e d f ro m th e g ra y - l e v e l im a g ea f t e r t h re sh o ld in g , a g a in u s in g a 3 x 3 o p e ra to r . Ath re sh o ld 0 o u a in e i s se t t o b e 2 5 % m a rk i n t h e r a n g e o fm a x i m u m a n d m i n i m u m g r a y - le v e l in t e n si t ie s i n t h ei m a g e , t h e n p i x e l s w h o s e g r a y - l e v e l in t e n s i t y v a lu e s a r ea b o v e t h e t h r e s h o ld a n d w h o s e 8 - n e i g h b o r c o u n t isb e lo w 8 m u s t b e o n t h e o u t l i n e [F ig . 3 (b ) ] . Th a t i s ,

0 o u t l i n e = gmin + 0.25(gmax - - grain),a n d

P C Foutline~ i f ( g e > 0 o u t l i n e )( 2 )a n d ( Z g p k > O o u f l in e ) < 8 .k = 0

In k d i s t r i b u t io n F in k :S e v e r a l r e s o lu t i o n s o f i n k d i s t r i b u t i o n i n t h e s i g n a t u r ei m a g e a r e e x t r a c t e d , c o a r s e [ F i g . 3 ( c ) ] , a n d f i n e[ F ig . 3 ( d ) ]. T h i s f e a tu r e i s m a i n l y u s e d i n t h e a l i g n m e n to f two s ig n a tu re s . I t i s im p o r t a n t t h a t d u r in g s ig n a tu rea l i g n m e n t o n l y t h e d o m i n a n t p ar t s a r e c o n s i d e r ed . T h e

c o a r se i n k d i s t r i b u t io n f ea tu re i s u se fu l fo r t r a n s l a t i o n a la n d l i n e a r s c a l i n g a l i g n m e n t , w h i l e t h e f i n e o n e i s t o b eu s e d i n n o n - l i n e a r sc a l in g . C o a r s e i n k d i s t r i b u t i o nfe a tu re i s e x t r a c t e d o n g r id s i z e o f 8 8 , a n d t h e f i n eo n e o n g r id s i z e 4 4 . A g r id i s f i l l e d i f t h e s i g n a tu rei m a g e p i x e l c o u n t i n s i d e t h e g r i d i s a b o v e 5 0 % o f t h eto t a l p ix e l s i n s id e t h e g r id .

Hig h p re ssu re r e g io n f e a tu re Fh p r:H i g h p r e s s u r e f e at u r e h a s b e e n u s e d b y A m m a r e t al. (14)to d e t e c t sk i l l e d fo rg e r i e s . I t i s e x t r a c t e d t o i n d i c a t er e g i o n s w h e r e m o r e e m p h a s i s h a s b e e n m a d e b y t h es ig n e r , u su a l l y t h e d a rk e r a re a i n t h e sc a n n e d im a g e . At h r e s h o ld 0 h pr i s s e t to b e 7 5 % m a r k b e t w e e n m a x i m u ma n d m i n i m u m g r a y - le v e l i n t e n si t ie s . P i x e l s o f g r a y - l e v e li n t e n s i t y v a lu e s l a rg e r t h a n t h re sh o ld a re c o n s id e re db e lo n g in g t o h ig h p re ssu re r e g io n s [F ig . 3 (e ) ] .

0 h p r = g m i n + 0 . 7 5 ( g m a x - - gmin),a n d

P C Fh p r , i fg e > 0 h p r . (3 ) Di re c t i o n a l f ro n t ie r s f e a tu re s F f rs :

T h e s e f e a tu r e s c o n t a i n d i r e c t i o n a l i n f o r m a t i o n as w e l l a ss i g n a t u re o u t l i n e s e g m e n t a t i o n i n f o r m a t i o n i n d i v i d u a ll y ,a n d p e n s t ro k e w i d t h v a r i a t io n i n f o r m a t i o n w h e nc o m b in e d . A p ix e l i s a s i g n a tu re n o r th f ro n t i e r i f i t sn o r t h n e i g h b o r i s b a c k g r o u n d , f o r e x a m p l e . N o r t h ,so u th , e a s t , we s t , n o r th -e a s t , n o r th -we s t , so u th -e a s t ,a n d so u th -we s t d i r e c t i o n a l f ro n t i e r s a re e x t r a c t e d[F ig . 3 ( f ) - (m ) ] .

3.1.2. Global geometric features. T h e f o l l o w i n gg l o b a l g e o m e t r i c f e a t u r e s a r e a l s o e x t r a c t e d , b ya n a l y z i n g f e a t u r e p r o j e c t i o n s a n d c o n n e c t e d f e a t u r ec o m p o n e n t s :

a r e a o f f e a tu re p ix e l s i n c o re , o u t l in e , h ig h p re ssu rer e g i o n , d i r e c t i o n a l f r o n t i e r s , c o a r s e , a n d f i n e i n kd i s t r i b u t io n ,

n u m b e r o f c o n s t i t u e n t p a rt s i n s i g n a t u r e a sd e t e r m i n e d f r o m c o a r s e a n d f i n e i n k d i s t r i b u t i o n ,

t h e c e n t r o i d l o c a t io n , a n d w i d t h a n d h e i g h t o f e a c hs ig n a tu re c o n s t i t u e n t p a r t s , a n d


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( l) ( m )Fig . 3 . Fea tu re im ages ex t ra c ted f rom a s igna tu re : (a ) co re , (b ) s igna tu re ou t l ine , (c ) coa rse reso lu t ion inkd is t r ibu t ion , (d ) f ine reso lu t ion ink d is t r ibu t ion , (e ) h igh p ressu re reg ion fea tu re , and ( f ) - (m) d i rec t iona lf ron t ie rs .


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14 K. HUAN G and H. YAN

t h e d y n a m i c w a r p i n g d i s t a n c e t o r e fe r e n c e t e m p l a t ec o m p u t e d d u r i n g f e a t u re a l i g n m e n t , to b e d e s c r i b e d l a te ri n Se c t i o n 3 .2 .

c u rv a tu re e s t im a t io n a t e a c h f e a tu re p o in t , c a n a l so b ec o n s id e re d .

3 .1 .3 . L oca l shap e f ea t ures . A se t o f r e c t a n g u la r g r id sa r e o v e r l a i d o n t o p o f e a c h s i g n a t u r e s h a p ere p re se n t a t i o n wh e n e x t r a c t i n g f e a tu re s . A c o a r se g r ido f s i z e 3 x 1 0 i s u se d a s t h e b a s i s s t ru c tu re (F ig . 4 ) .Us in g t h e c o a r se g r id , t h e im a g e i s d iv id e d i n to t h re eh o r i z o n t a l z o n e s r o u g h l y c o r r e s p o n d i n g t o u p p e r ,m i d d l e , a n d l o w e r z o n e s o f t h e w r i t i n g , a n d i n t o t e nv e r t i c a l z o n e s w i t h w i d t h o f a n a v e r a g e l o w e r - c a s el et te r . T h e h i g h e s t r e s o l u t io n g r i d is 6 2 0 . A m e d i u mg r id o f s i z e 4 1 5 i s a l so u se d . T h e b o rd e r s o f t h e se g r id sa re fu z z i f i e d t o r e d u c e t h e e f f e c t o f a b ru p t c h a n g e s o nf e a tu r e m a t c h i n g i f s e v e r e m i s a l i g n m e n t o c c u r s. T h ee x t e n t o f f u z z y r e g i o n i s d e t e r m i n e d b y a p e r c e n t a g ef a c t o r p f , w h e r e O < p f < l . T h e h o r i z o n t a l e x t e n t e q u a l st h e w i d t h o f g r i d b o x W g m u l t i p l i e d b y pf , a n d t h ev e r t i c a l e x t e n t e q u a l s t h e h e i g h t o f g r i d b o x H gm u l t i p l i e d b y pf . D u r i n g o u r e x p e r i m e n t , t h e v a l u e o fp f i s se t to 0 .1 5 . In s id e e a c h g r id , t h e n u m b e r o f f e a tu rep i x e l s a r e c o u n t e d . F o r f e a t u r e p i x e l s n e a r b o u n d a r yre g io n s , a l i n e a r fu z z y we ig h t in g f a c to r i s m u l t i p l i e d .T h e f e a tu r e v a l u e i s t h e n n o r m a l i z e d b y t h e p e r i m e t e rv a l u e o f t h e g r i d b o x .

Th e fo l l o win g d i r e c t i o n a l f i l t e r s a re a p p l i e d t o c o re ,o u t l i n e f e a t u r e s , a n d f r o n t i e r f e a t u r e s w h e n t h e s ef e a t u r e s a r e i n d i v i d u a l l y u s e d a s s i g n a t u r e s h a p ere p re se n t a t i o n :

O t h e r c o m m o n f e a t u r e v e c to r c o n s t r u c t i o n t e c h n i q u e sf o u n d m o s t l y i n c h a r a c t e r r e c o g n i t i o n w o r k s , s u c h a s

7 -Hg~L

~ - w g - ~

Loca l shape fea tu reex t rac t ion g r idFuzzy weigh t ing funct io n

in eac h g r id box1

Fig. 4. Local shape feature extraction grid with fuzzifiedborder regions. The regular grid location is indicated by thickborder line. The extent of fuzzy region is indicated by thinborder line. The lin ear fuzzy w eighting scheme is illustrated inthe lower part.

3 .2 . F ea t ure a l i gnme ntTo e f fe c t i v e ly r e d u c e i n t r a p e r so n a l v a r i a t i o n s , t h e se t

o f r e fe re n c e s ig n a tu re s a s we l l a s i n p u t t e s t s i g n a tu re sn e e d t o b e c a re fu l l y a l i g n e d b e fo re l o c a l sh a p e f e a tu ree x t r a c t i o n a n d s t a t i s ti c a l m o d e l c o n s t ru c t i o n o r v e r i f i c a -t i o n . T h i s i s a n a r e a w h i c h h a s n o t b e e n w e l ld o c u m e n t e d i n t h e p a s t .

T h e h o r i z o n t a l a n d v e r t i c a l p r o j e c t i o n s o f t h eb i n a r i z e d i n k a r e a i m a g e a r e o b t a i n e d f o r a l l r e f er e n c es a m p l e s b e l o n g i n g t o t h e s a m e s i g n a t u re . O n e s e t o fp r o j e c t i o n s a r e s e l e c t e d a r b i t r a r i l y a s a c o m m o nre fe re n c e t e m p la t e , a n d p ro j e c t i o n s f ro m o th e r r e fe re n c esa m p le s a re t r a n s fo rm e d a g a in s t t h e t e m p la t e t o f i n d t h ef u n c t i o n w h i c h m a x i m i z e s c o r r e l a ti o n , h e n c e t o r e g i st e rt h e se s i g n a tu re sa m p le s i n a b e t t e r wa y .

T h e t r a n s f o r m a t i o n f u n c t i o n s c o n s i d e r e d a r e l i n e a rt r a n s l a t i o n , sc a l i n g , a n d n o n - l i n e a r sc a l i n g b y e l a s t i cm a tc h in g . On th e c o a r se i n k a re a l e v e l , l i n e a r t r a n s l a -t i o n s o f s i g n a tu re c o n s t i t u e n t p a r t s a r e p e r fo rm e d . T h i ss t e p i s n e c e s s a r y s i n c e f r o m o b s e r v a t i o n s o f m a n yg e n u i n e s i g n a t u r e s c o n s i s t i n g o f m u l t i p l e p a r t s , t h el e n g t h s o f w h i t e s p a c e s e p a r a t i n g t h e s e p a r t s a r eu n e x p e c t e d l y v a r i a b l e . L i n e a r s c a l i n g i s p e r f o r m e di n f e a t u r e s p a c e b y a p p l y i n g f i x e d s i z e g r i d s o nd i f f e r e n t s i z e f e a t u r e i m a g e s d u r i n g l o c a l f e a t u r ee x t r a c t i o n , r a th e r t h a n sc a l i n g i n im a g e sp a c e t o r e d u c ed i s to r t i o n s .

Mo re d e t a i l e d s ig n a tu re im a g e r e g i s t r a t i o n i s p e r -f o r m e d b y n o n - l i n e a r sc a l in g . A t w o - d i m e n s i o n a l s u r -f a c e s c a l i n g f u n c t i o n i s s e a r ch e d b y e l a s ti c m a t c h i n g o fth e h o r i z o n t a l a n d v e r t i c a l p ro j e c t i o n s o f t h e f i n er e s o l u t i o n i n k a r e a i m a g e a g a i n s t t h o s e f r o m t h ec o m m o n t e m p l a t e u s i n g a d y n a m i c p r o g r a m m i n g b a s e dt e c h n iq u e . 6 ) T h e a c c u m u l a t e d w a r p i n g d i s t a n c e i su t i l i z e d a s a g lo b a l f e a tu re wh e n n o n - l i n e a r sc a l i n g i sp e r f o rm e d . T h e n o n - l i n e a r s c al i n g m e t h o d , w h i c h i t s e l fc a n se rv e a s a s i g n a tu re c l a ss i f i e r , i s t h e su b j e c t o f asepara te paper .

4 . T R A I N I N G T H E N E U R A L N E T W O R K C L A S S I F IE R

S i g n a t u r e v e ri f i c at i o n i s a p r o b l e m i n w h i c h w e c a nn e i th e r c o m p le t e ly s p e c i fy t h e t ru e sa m p le sp a c e n o r th ef a l s e o n e . N e u r a l n e t w o r k i s p r o v e n t o b e r o b u s t a n df l e x ib l e i n m a n y a p p l i c a t i o n s . I t h a s t h e a b i l i t y t og e n e r a l i z e i t s s o l u t i o n s p a c e b y i n t e r p o l a t i o n a n de x t ra p o la t i o n . I f t r a in e d a d e q u a t e ly , a c c u ra t e a n d m e a n -i n g f u l r e s u l t s c a n b e o b t a i n e d f r o m a n e u r a l n e t w o r kc l a s si f ie r w h e n p r e v i o u s l y u n s e e n s a m p l e s a r e a p p l ie d .A p e r so n c o u ld s ig n h i s o r h e r s i g n a tu re i n t h re e o r m o res ty l e s , wi th so m e s ty l e s o c c u r r in g m o re f r e q u e n t ly t h a no th e r s . Cu r re n t ly we h o p e t h a t a g e n e ra l i z e d n e u ra ln e two rk c a n b e t r a in e d t o c o v e r a l l t h e se v a ry in g s ty l e s .Mu l t i - l a y e r p e rc e p t ro n i s se l e c t e d a s t h e s t ru c tu re fo r t h es ig n a tu re c l a ss if i e r .


7/9

Off-line signature verification based on geometric feature extraction 15

4.1. Perturbation sample generationTo m o d e l p ra c t i c a l s i t u a t i o n s , a sm a l l se t o f r e fe re n c e

s a m p l e s f r o m t h e g e n u i n e s i g n a t u r e s e t a n d a r t i f i ci a l lyg e n e ra t e d sa m p le s f ro m th e se r e fe re n c e s a re u se d a st r a i n i n g d at a . S l i g h t l y p e r t u r b e d v e r s i o n s o f g e n u i n es i g n a t u re s a r e u s e d a s g e n u i n e t r a i n i n g sa m p l e s . H e a v i l yd i s to r t e d v e r s io n s a re u se d a s fo rg e ry t r a in in g sa m p le s .T h e p e r t u r b a t i o n s a n d t h e i r c o r r e s p o n d i n g p a r a m e t e r sa re s lan t d is tor t ion 0stunt , s ize d is tor t i on Sx and Sy fo rsc a l i n g i n h o r i z o n t a l a n d v e r t i c a l d i r e c t i o n s , ro t a t i o nd i s to r t i o n 0 ro ta te a n d p e r sp e c t i v e v i e w d i s to r t i o n A.C o n s i d e r t h e w r i t i n g p r o ce s s w h e r e t h e w r i t e r m a y h o l dth e p e n a t s l i g h t l y d i f f e re n t p o s i t i o n s a n d s l a n t a n g l e se a c h t im e th e s ig n a tu re i s re p ro d u c e d , s i z e , s l a n t a n dp e r sp e c t i v e v a r i a t i o n s a re n a tu ra l l y a d d e d to t h e p e nt r a c e im a g e . He n c e i t i s u se fu l t o a l l o w su c h k in d s o fsm a l l p e r tu rb a t i o n s .

T h e g r a y - l e v e l i m a g e i t s e l f i s d i s to r t e d w h e ng e n e r a t i n g a r t i f i c i a l t r a i n i n g s a m p l e s . T h e c e n t r o i dl o c a t i o n o f e a c h s i g n a t u r e i m a g e u n d e r p e r t u r b a t i o nre m a in s i n v a r i a n t . P ix e l c o o rd in a t e s i n t h e d i s to r t e dim a g e r e c t a n g l e a re r e v e r se m a p p e d to t h e o r ig in a li m a g e s p a c e u n d e r p e r t u r b a t i o n e q u a t i o n s , t h e n t h e g r a y -l e v e l v a l u e o f t h e p i x e l i s d e t e r m i n e d b y b i - l i n e a rin t e rp o l a t i o n ,u S) Ta b le 1 sh o ws th e r e v e r se m a p p in ge q u a t i o n s a n d T a b l e 2 s h o w s t h e v a l u e s o f p e r tu r b a t i o np a r a m e t e r s c h o s e n d u r i n g e x p e r i m e n t .4.2. Neural network training

T h e m u l t i - l a y e r p e r c e p t ro n n e t w o r k i s t r a i n e d w i t h 8e n r o l l e d r e f e r e n c e s a m p l e s a n d 3 2 0 o r s o d e r i v e ds a m p l e s a s g e n u i n e t r a i n i n g d at a , 6 4 0 d e r i v e d s a m p l e sp l u s r a n d o m s a m p l e s , d e r i v e d d a ta f r o m o t h e r s i g n a t u rec l a sse s ( t o t a l 3 0 0 0 ) a s fo rg e ry t r a in in g d a t a. Th e t r a in e dn e t w o r k s h o u l d d r a w a c l e a r b o u n d a r y f o r t h e t r a in e dg e n u in e s a n d t h e r e s t sh o u ld b e fo rg e ry sp a c e .

n o t a f f e c t t h e e x i s t i n g t r a in e d sy s t e m . Ea c h v e r i f i e rc o n s i s t s o f se v e ra l s im p le t h re e - l a y e r p e rc e p t ro n n e t -w o r k s , o n e n e t w o r k p e r f e a t u r e r e s o l u t i o n p l u s ad e c i s i o n n e t w o r k w h o s e f u n c t i o n i s t o c o m b i n e t h ere sp o n se s f ro m fe a tu re n e two rk s a n d t o p ro d u c e a f i n a lc o n f id e n c e r a t i n g (F ig . 5 ) .

F o r f e a t u re n e t w o r k s , t h e n u m b e r o f i n p u t s e q u a l s t h efe a tu re v e c to r s i z e, wh ic h i s d e t e rm in e d f ro m th e f e a tu rer e s o l u ti o n . T h e n u m b e r o f u n i t s i n t h e s i n g l e h id d e nl a y e r i s d e t e r m i n e d f r o m e x p e r i m e n t s u c h t h a t t h en e two rk wi l l t r a in a d e q u a t e ly . I t i s c h o se n t o b e 2 0 .Th e re a re two o u tp u t u n i t s , c o r re sp o n d in g t o t ru e a n dfa l se re sp o n se n o d e s . T h e d e c i s io n n e two rk t a k e s o u tp u t sf r o m a l l f e a t u r e n e t w o r k s a n d i t h a s a n i n p u t l a y e r o fs i z e 6 , o n e h id d e n l a y e r o f s i z e 6 , a n d a s i n g l e o u tp u t .

Th e sy s t e m f lo w d i a g ra m i s i l l u s t r a te d i n F ig . 6 .D u r i n g t r a i n i n g p ha s e , e i g h t g e n u i n e r e f e r e n c e s a m p l e sa n d d e r iv e d d a t a f ro m th e m a re a p p l i e d t o t h e f e a tu ren e two rk s u n d e r su p e rv i se d l e a rn in g . Th e t h re e f e a tu ren e two rk s i n e a c h v e r i f i e r a r e t r a in e d i n d iv id u a l ly . Th er e s p o n s e s f r o m f e a t u r e n e t w o r k s a r e r e c o r d e d a n da p p l i e d t o t h e d e c i s i o n n e t w o r k , a g a i n u n d e r s u p e r v i s e dl e a rn in g . Th e s ig n a tu re s t a t i s t i c a l m o d e l a s we l l a s t h ed i s c r i m i n a n t m e a s u r e a r e s t o r ed i n t h e n e t w o r k c o n n e c -t i o n we ig h t s . Glo b a l f e a tu re s a re c o l l e c t e d b u t n o tc u r re n t ly u t i l iz e d .

On c e t r a in e d , t h e v e r i f i e r p e r fo rm a n c e i s e v a lu a t e d fo rb o t h r a n d o m f o r g e r i e s a n d t a r g e t e d f o r g e r i e s . A l lg e n u in e s ig n a tu re s a n d a l l fo rg e ry s ig n a tu re s a re u se da s t e s t i n g d a t a . Un d e r r a n d o m fo rg e ry t e s t , a l l d a t a se t se x c e p t t h e o n e w i t h i d e n t i fi c a t i o n n u m b e r t h e s a m e a sth e v e r i f i e r a r e a p p l i e d . Th e a v e ra g e f a l se a c c e p t a n c era t e fo r e a c h s ig n a tu re c l a ss i s j u s t b e lo w 0 .0 5 % , fo r at e s t se t s iz e o f 3 3 6 0 sa m p le s . Un d e r t a rg e t e d fo rg e ryt e s t, t h e t e s t s i z e i s 2 4 g e n u in e sa m p le s a n d 1 4 4 fo rg e rysa m p le s , t h e a v e ra g e c o r re c t c l a s s i f i c a t i o n ra t e i s a b o u t9 0 % wh e n n o d a t a r e j e c t i o n i s a l l o we d (Ta b le 3 ) .

5 . S Y S T E M I M P L E M E N T A T I O N A N D R E S U L T S 6. CONCLUSIONSI n d i v i d u a l s i g n a t u re v e r i f i er i s c o n s t r u c t e d f o r e a c h A n o f f - l i n e s i g n a tu r e v e r i f ic a t i o n m e t h o d b a s e d o n

e n ro l l e d s ig n a tu re c l a ss . Ad d i t i o n a l u se r e n ro l lm e n t wi l l g e o m e t r i c f e a tu re e x t r a c t i o n o f l o c a l i z e d , a l i g n e d sh a p eTable 1. Reverse m apping equations for all perturbation operations in artificial training sam ple generation. (x, y) is the co-ordinateof a pixel in the distorted image, and (x', 3/) is the corresponding co-ordinate location in the original imagePertubation Reverse -ma ppin g equationSlant x t=x-y t a n O s t a n t , y'=yRotation x ' =x C O S 0 r o t a t e - y sin 0rotate 3/= y c o s 0 r o t a t e 4-X si n 0 r o t a t eScaling x ' = x / S x 3/--y/SyPerspec t ive x '=xM(A-5) , 3/=yM(A-5)

Table 2. Perturbation parameter values used in generating artificial training sam plesPertubation Parameter Slight distortions Heavy distortionsSlant 0 s l a n 0; 1.5; d:3.0Rotation 0rotate 0; 1 .5; 3 .0 Scaling Sx 1; 10.05; 10.1Scaling Sy 1; 1 4-0.05; 14-0.1Persp ective A -4-(20-4-1); +(20-4 -2);

5.0; +10.0; 4-20.05 .0; -4-10.0; -t-20.00.4; 0.5; 0.6; 0.7; 1.3 ; 1.4; 1.5 ; 1.60.4; 0.5; 0.6; 0.7; 1.3 ; 1.4; 1.5; 1.6(1 0 1 ) ; (1 0 2 ) ; (1 3 +1 )


8/9

1 6 K . H U A N G a n d H . Y A N

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. . .. .. .. .. .. .. .. . ! . .. .. .. .. .. .. .. . .- . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .- . .. .. .. .. .. .. .. .. . - . .. .. .. ..

. . . . . . . . . .

i i t i ti i i i i, : . . . . . . . . . : . . . . . . . . . : ......... ,ii i t i i

i t i Ir

Fig. 5. Neu ral netwo rk classif ier for s ignatu re verification. I t consists of several feature network s and adecision network.

I En ro l lment sam ples ] [ Input test s ignatures 1

l lPerturbat ion _ S h a p e f e a t u r e a n dg e n e r a t e d g l o b a l f e a t u r egens & fa ts - extract ion1

S h a p e f e a t u r e a l ig n m e n t I1

Refe rence mode lconstruct ion usingneu ra l ne two rks

Loca l shape fea tu re Iextract ion usingfuzzy g r ds

Ver i f ica t ionaga ins t mode l us ingneu ra l ne two rks1Con f i dence ra t i ng ou tpu t

Fig . 6 . S igna ture ver i f i ca t ion sys tem d iagram. I t shows thef low pa th fo r b o th the t ra in ing phase ( le f t por t ion) and tes t ingphase ( r igh t por t ion) o f the sys tem.

f e a t u r e s a n d n e u r a l n e t w o r k c l a s s i fi c a t i o n is d e s c r i b e da n d i t s e f f e c t iv e n e s s i s e v a lu a t e d o n a m o d e r a t e s i z ee x p e r i m e n t a l s i g n a t u r e d a ta b a s e . T h e e x p e r i m e n t a lr e s u l t s s h o w t h a t t h e s i g n a t u r e c l a s s i f i e r s c o n s t r u c t e dw i t h t h e p r o p o s e d m e t h o d a r e v e r y e f f e c t i v e i n r a n d o m

Table 3 . Exper imenta l resu l t on s igna ture da tabase fo r ta rge tedforgery tes tTotal false Total false Total genu ines Total forgeriesre jec t s accep ts56 357 504 3024A v e r a g e f a l s e A v e r a g e f a l s e T y p e I e r r o r T y p e I I e r r o rrejects accepts3 17 11.1% 11.8%

f o r g e r y d e t e c t i o n , h o w e v e r i t s p e r f o r m a n c e i s r e d u c e dw h e n t a r g e t e d f o r g e r y a r e a p p l i e d , e s p e c i a l l y w h e ns k i l l e d t r a c e d f o r g e r i e s a r e i n v o l v e d . A s a f i r s t s t a g ev e r i f i e r , i t c a n l a b e l a s u i t a b l e a m o u n t o f i n p u t s a sq u e s t i o n a b l e s a n d p a s s t h e m t o a m o r e d e t a i l e d b u t ti m e -c o n s u m i n g v e r i f i e r , t h u s i m p r o v e s b o t h t h e s p e e d a n da c c u r a c y o f t h e c o m p l e t e s y s t e m .A c k n o w l e d g e m e n t s - - T h e a u t h o r s w i s h t o t h a n k a l l w h oh a v e c o n t r i b u t e d t o t h e s i g n a t u r e d a t a b a s e , a n d D . J .D i n g f o r v a l u a b l e s u g g e s t i o n s a n d d i s c u s s i o n s.

R E F E R E N C E S

1 . R . P l a m o n d o n a n d G . L o r e t t e , A u t o m a t i c s i g n a t u r everif icat io n and writer identif icat ion: the state of the art ,Pattern Recognit ion 22, 107-131 (1989).2. G. Pir lo, Algori thms for s ignature verif icat ion, F u n d a -menta ls in Ha ndwri t ing Recogni t ion , S. Impedovo, ed., pp.435-454 . Spr inger, Be din (1994).3 . R . P lamondon and G. Lore t te , Des ign ing an au tomat ics igna ture ver if ie r : p rob lem def in i t ion and sys tem descr ip -


9/9

O f f - l i n e s i g n a t u re v e r i f i ca t i o n b a s e d o n g e o m e t r i c f e a t u r e e x t r a c t io n 1 7

t ion , C o m p u t e r P r o c e s s i n g o f H a n d w r i t i n g , R . P l a m o n d o na n d C . G . L e e d h a m , e d s , p p . 3 - 2 0 . W o r l d S c i e n t i f i c ,S i n g a p o r e ( 1 9 9 0) .4 . F . L e c l e r c a n d R . P l a m o n d o n , A u t o m a t i c s i g n a t u reve r i f ica t ion : the s ta te o f the a r t 1989-1993 , In t . J . Pa t t e r nR e c o g n i t i o n A r t i f i c . I n t e l l . 8 , 643 -659 (1994) .5 . P . de Bruyne and R. Fo r te , S igna tu re ve r i f ica t ion wi the l a s t i c i m a g e m a t c h i n g , I n t . C a r n a h a n C o n f . o n S e c u r i t yT e c h n o l o g y : C r i m e C o u n t e r m e a s u r e s , G o t h e n b u r g , S w e -d e n , 1 1 3 - 1 1 8 . U n i v e r s i t y o f K e n t u c k y , L e x i n g t o n , K Y ,U.S .A. (1986) .6 . E N ouboud , Handw r i t ten s igna tu re ve r i f ica t ion : a g loba la p p r o a c h , F u n d a m e n t a l s i n H a n d w r i t i n g R e c o g n i t i o n , S .Impedovo , ed . , pp . 455 -459 . Sp r inge r , Ber l in (1994) .7 . R . S a b o u r i n , M . C h e r i e t a n d G . G e n e s t , A n e x t e n d e d -s h a d o w - c o d e b a s e d a p p r o a c h f o r o f f - l i n e s i g n a t u r e v e r -i f i c a t i o n , P r o c . I C D A R ' 9 3 : I n t. C o n f . o n D o c u m e n tA n a l y s i s a n d R e c o g n i t i o n , T s u k u b a S c i e n c e C i t y , J a p a n ,1 - 5 . I E E E C o m p u t e r S o c i e t y P r e s s ( 1 99 3 ).8 . R . S a b o u r i n a n d J . P . D r o u h a r d , O f f - l i n e s i g n a t u r ev e r i f i c a t i o n u s i n g d i r e c t i o n a l P D F a n d n e u r a l n e t w o r k s ,P r o c . 1 1 t h I A P R I n t. C o n f . o n P a t t e r n R e c o g n i t i o n , 3 2 1 -325 (1992).9 . R . Sabour in , R . P lamondon and L . Beaumie r , S t ruc tu ra li n t e r p r e t a t i o n o f h a n d w r i t t e n s i g n a t u r e i m a g e s , In t . J .P a t t e r n R e c o g n i t i o n A r t i f i c . l n t e l l . 8 , 709 -748 (1994) .

1 0. M . A m m a r , P r o g r e s s i n v e r i f c a t i o n o f s k i l lf u l ly s i m u l a t e dh a n d w r i t t e n s i g n a t u r e i m a g e s , I n t . J . P a t t e r n R e c o g n i t i o nA r t i f i c . I n t e l l . 5, 337-351 (1991).11 . S . L ee a nd J . C . Pan , Of f i ine t rac ing and rep resen ta t io n o fs igna tu res , I E E E T r a n s . S y s t e m s M a n C y b e r n e t . 2 2 , 7 5 5 -771 (1992).12 . M. A mm ar , Y. Yosh ida and T . Fukumura , S t ruc tu ra ld e s c r i p t i o n a n d c l a s s i f i c a t io n o f s i g n a tu r e i m a g e s , P a t t e r nR e c o g n i t i o n 23 , 697 -710 (1990) .

1 3. C . D . L e e a n d R . A . A b b e y , C l a s s i fi c a t i o n a n d I d e n t i c a t io no f H a n d w r i t i n g . D . A p p l e t o n a n d C o m p a n y , N e w Y o r k( 1 9 2 2 ) .14 . M. Ammar , Y. Yosh ida and T . Fukumura , A new e f fec t ivea p p r o a c h f o r o f f - l i n e v e r i f i c a t i o n o f s i g n a t u r e s b y u s i n gp ressu re fea tu res , P r o c . 8 t h I n t . C o n f . o n P a t t e r n R e c o g n i -t ion , Par is , 566 -569 (1986) .1 5. R . C . G o n z a l e z a n d R . E . W o o d s , D i g i t a l I m a g eP r o c e s s i n g , A d d i s o n - W e s l e y , R e a d i n g , M a s s a c h u s e t t s(1992).1 6. H . S a k o e a n d S . C h i b a , D y n a m i c p r o g r a m m i n g a l g o r i th mo p t i m i z a t i o n f o r s p o k e n w o r d r e c o g n i t i o n , I E E ET r an s . A c o u s t . S p e e c h S i g n a l P r o c e s s . A S S P - 2 6 , 4 3 ~ 4 9(1978).

A b o u t t h e A u t h o r - - K A I H U A N G r e c e iv e d hi s B .S c . d e gr e e in C o m p u t e r S c i e n ce an d P u r e M a t h e m a ti c s in1 9 91 a n d B . E . ( H o n o r s ) d e g r e e i n E l e c t r i c a l E n g i n e e r i n g i n 1 9 9 3, b o t h f r o m t h e U n i v e r s i t y o f S y d n e y . H e i sc u r r e n t l y a P h . D . s t u d e n t i n t h e D e p a r t m e n t o f E l e c t r i c a l E n g i n e e r i n g a t t h e U n i v e r s i t y o f S y d n e y . H i s r e s e a r c hi n t e r e s t s i n c l u d e p a t t e r n r e c o g n i t i o n , s i g n a l a n d i m a g e p r o c e s s i n g a n d a u t o m a t i c h a n d w r i t t e n s i g n a t u r eve r i f ica t ion .

A b o u t t h e A u t h o r - - H O N G Y A N r e c e i v e d h i s B . E . d e g r e e f r o m N a n k i n g I n s ti t u te o f P o s t s a n dT e l e c o m m u n i c a t i o n s i n 1 9 8 2, M . S . E . d e g r e e f r o m t h e U n i v e r s i t y o f M i c h i g a n i n 1 9 84 , a n d P h . D . d e g r e ef rom Y a le Un ive rs i ty in 1989 , a l l in e lec t r ica l eng inee r ing . F rom 1987 to 1989 he was a re sea rch sc ien t i s t a tG e n e r a l N e t w o r k C o r p o r a t i o n , N e w H a v e n , C T , U . S . A . , w h e r e h e w o r k e d o n d e v e l o p i n g a C A D s y s t e m f o ro p t i m i z i n g t e l e c o m m u n i c a t i o n s y s t e m s . S i n c e 1 9 8 9 h e h a s b e e n w i t h t h e U n i v e r s i t y o f S y d n e y w h e r e h e i sc u r r e n t l y a r e a d e r i n e l e c t r i c a l e n g i n e e ri n g . H i s r e s e a r c h i n t e r e s t s i n c l u d e m e d i c a l i m a g i n g , s i g n a l a n d i m a g ep r o c e s s i n g , c o m p u t e r v i s i o n , n e u r a l n e t w o r k s a n d p a t t e r n r e c o g n i ti o n . H e i s a n a u t h o r o f m o r e t h a n 1 0 0 t e ch n i c a lp a p e r s i n t h e s e a r e as . D r Y a n i s a s e n i o r m e m b e r o f I E E E , a n d a m e m b e r o f S P I E , I n t e rn a t i o n a l N e u r a l N e t w o r kS o c i e t y ( I N N S ) , P a t t e r n R e c o g n i t i o n S o c i e t y ( P R S ) , a n d S o c i e t y o f M a g n e t i c R e s o n a n c e ( S M R ) .

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