Novel fusion approach for Face detection using … · Novel fusion approach for Face detection using Gabor and LBP. Bhavna Pancholi Assistant Professor ... [email protected]

Novel fusion approach for Face detection using Gabor and LBP

Bhavna Pancholi

Assistant Professor,

Electrical Department,

The M S University, Baroda, Gujarat, India

[email protected]

Mitul Modi

PG Student,

Electrical Department,

The M S University, Baroda, Gujarat, India

[email protected]

Abstract

Face detection is the first stage of automatic face

recognition system. Face Detection is the process of finding and localizing faces inside a given image. Face

detection plays major role in face recognition, facial

expression recognition, head-pose estimation, human-

computer interaction, etc. Various methods are

available for detecting faces. Gabor feature extraction

is one of the classification based face detection method

to detect face efficiently. In this paper, Improvement

has been suggested in Gabor feature extraction

algorithm i.e. LBP based input is given to Gabor

feature. Extensive experiments are conducted on

several face databases and verified the effectiveness of

the proposed approach.

Key Words: Face detection, Gabor feature extraction,

LBP (Local Binary Pattern)

1. Introduction Face detection is a computer technology that

determines the locations and sizes of human faces in

arbitrary images. It detects facial features and ignores anything else, such as buildings, trees and bodies. Even

though face detection has been studied for a long time,

the researchers are still trying to develop a more

advanced face detector. This is due to the challenges

that can fail even the more recent face detectors. These

challenges can be listed as follows [1, 2]

o Facial expression

o Head – pose

o Presence or absence of structural elements

o Occlusion

o Image orientation

o Imaging condition

Various methods have been proposed to identify

face in a single image of intensity or color images. An

important issue is how to evaluate the performance of

the proposed detection methods [3]. Many papers compare the performance usually in terms of detection

and false alarm rates. Many metrics have been adopted

to evaluate algorithms, such as testing time, execution

time, the number of samples required in training, and

the ratio between detection rates and false alarms. In

general, detectors can make two types of errors: false

negatives in which faces are missed resulting in low

detection rates and false positives in which an image is

declared to be face.

Gabor face feature extraction is one the most

popular methods to detecting faces. In this paper,

modification has been suggested i.e. LBP (Local

Binary Pattern) based input is applied to Gabor feature

2. Existing Work

Fig. 1. Block Dia. Face Detection Using Gabor

Feature

Fig. shows block diagram of Gabor face feature

extraction method to detect face. The maximum

intensity points dynamically on each filtered image are

found and are marked as face points. If the distance is

minimum between these face points then system

reduces the points. After that the system accesses the

gallery and identify the image if the distance between

points gets matched.

Mitul Modi et al, Int.J.Computer Technology & Applications,Vol 5 (2),646-649

IJCTA | March-April 2014 Available [email protected]

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ISSN:2229-6093

mailto:[email protected]

mailto:[email protected]

2.1 Pre – processing: Mainly this step of face detection includes grab input

image and normalization. Normalization is nothing but

one type of contrast stretching. It generally prepared

image for feature extraction. It also minimized intra – class difference and detection error propagation.

2.2 Gabor Feature Extraction Gabor feature extractor can capture salient visual

properties such as spatial localization, orientation

selectivity, and spatial frequency characteristics.

Considering these excellent capacities and its great

success in face detection [4],

Where, gives the frequency,

gives the orientation

is the oscillatory wave function whose real

part and imaginary part are cosine function and

sinusoid function [5], respectively. The factor σ = kf

makes sure the filter spatial range of action is partial

correspondingly to the central frequency f.

Fig. 2 Gabor Feature Extractor

Applying Gabor feature extractor [6], [7] to single face

input image. 40 Gabor extractor is shown in fig. 2

Fig.3. After applying gabor extractor

2.3 Face Point and Minimize face points After applying Gabor extractor, 40 images are needed

with various angles and orientations. Then the

maximum intensity points are found in each image.

After that 40 points on image calculated and to find

these face points, the following equation is used.

Once face points are marked on face image. Then the

distance is evaluated to reduce the points. The

minimum distance possible between two points is

defined to minimize the number of points.

Fig. 4 Minimizing face points

2.4 Matching Now the distances between face points are evaluated by

following equation.



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Fig. 5 Distance Measure

The distances of the selected points are compared with

gallery database; if the distances get matched with

database the face is detected.

Fig. 6 Result of Gabor Feature Extraction

3. Local Binary Pattern (LBP)

LBP is one of the most powerful descriptors to

represent local structures. Due to its advantages, i.e., its

tolerance of monotonic illumination changes and its

computational simplicity, LBP has been successfully

used for many different image analysis tasks. Local

binary pattern operator, first introduced by Ojala et al.

[8], was based on the assumption that texture has

locally two complementary aspects, a pattern and its

strength. In that work, the LBP was proposed as a two-

level version of the texture unit [9] to describe the local

textural patterns.

This operator works with the eight neighbours of a

pixel, using the value of this centre pixel as a threshold.

If a neighbour pixel has a higher gray value than the

centre pixel (or the same gray value) than a one is

assigned to that pixel, else it gets a zero.

6 5 2

7 6 1

9 8 7

Example

1 0 0

1 0

1 1 1

Threshold

1 2 4

128 8

64 32 16

Weights

Pattern = 11110001

LBP = 1 + 16 + 32 + 64 + 128 = 241

Contrast Measure C = (((6+7+8+9+7)/5) - ((5+2+1)/3))

= 4.7

One limitation of the basic LBP operator is that its

small 3 × 3 neighbourhood cannot capture dominant

features with large-scale structures. To deal with the

texture at different scales, the operator was later

generalized to use neighbourhoods of different sizes

[10]. A local neighbourhood is defined as a set of

sampling points evenly spaced on a circle.

4. Proposed Work Fig. 7 shows the block diagram of face detection in

which new approach is proposed i.e. initially LBP

based input is given to the Gabor feature extractor. LBP

defines the texture pattern of face and it is very useful

for Gabor feature extractor to detect face.

Fig.7. Proposed Approach

5. Experiment Results We have performed various face detection experiments,

using the proposed technique. Our detection system has

been tested on various face image datasets [11] and

satisfactory results have been obtained.



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(a)

(b) Fig. 8 Results of Proposed Method

6. Comparative Analysis Table shown below gives comparison between Gabor

method and fusion method which suggested in this

paper.

Table 1

TAR : True Acceptance Ratio

TRR : True Rejection Ratio

FAR : False Acceptance Ratio

FRR : False Rejection Ratio

7. Conclusion & Future Work: Face detection has been an attractive field of research

for both neuroscientists and computer vision scientists.

In this paper LBP operator is used for defining the

texture pattern of faces before images are applied to

Gabor feature extractor. Using this fusion, face can be

easily detected with accuracy of 96.5% which suggest

some improvement in the process of face detection. Insufficient data is the major disadvantage of this

fusion. Half face or partial face doesn’t have the

necessary information that identify it as separate face.

This insufficient data is the reason of this low

efficiency. Improvement is suggested that partial face

data could also be given to the neural network for the

training purpose which will improve the result of the

proposed method.

8. References [1] M. Yang, D. Kriegman, and N. Ahuja, “Detecting Faces

in Images: A Survey,” IEEE Transaction Pattern Analysis

and Machine Intelligence, vol. 24, no. 1, pp. 34-58, 2002.

[2] E. Hjelmas, and B.K. Low, “Face Detection: A Survey,” Computer Vision and Image Understanding, vol. 83, pp. 236-

274, 2001.

[3] Ming-Husan Yang, David J.Kriegman, and Narendra

Ahuja, “Detecting Faces in Images: A Survey “, IEEE transaction on pattern analysis and machine intelligence,

vol.24 no.1, January 2002.

[4] Phillips P. J., Moon H., Rauss P., and Rizvi. SA.: The

FERET Evaluation Methodology for Face-Recognition Algorithms. Proceedings of Computer Vision and Pattern

Recognition, Puerto Rico, (1997) 137-143

[5] Li S. Z., Zhu L., Zhang Z.Q., Blake A., Zhang H. J., and Shum H.: Statistical Learning of Multi-View Face

Detection. In Proceedings of the 7th European Conference on

Computer Vision. 2002

[6] B. Kepenekci, F. B. Tek, and G. B. Akar. “Occluded face recognition by using gabor features”. In 3rd COST 276

Workshop on Information and Knowledge Management for

Integrated Media Communication, Budapest, October 2002.

[7] L.Wiskott, J. M. Fellous, N. Kuiger, and C. von derMalsburg, “Face recognition by elastic bunch graph

matching,” Pattern Analysis and Machine Intelligence, IEEE

Transactions on, vol. 19, no. 7, pages 775–779, July 1997.

[8] Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature

distributions. Pattern Recognition. 29(1), 51–59 (1996)

[9] Wang, L., He, D.C.: Texture classification using texture

spectrum. Pattern Recognition. 23, 905– 910 (1990) [10] T. Ojala, M. Pietik¨ainen, and T. Maenpaa,

“Multiresolution gray-scale and rotation invariant texture

classification with local binary patterns,” IEEE Trans.

Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002.

[11] University of essex,uk faces94 face recognition data.



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Novel fusion approach for Face detection using … · Novel fusion approach for Face detection using Gabor and LBP. Bhavna Pancholi Assistant Professor ... [email protected]