Applied mathematics in Engineering, Management and Technology 2 (2) 2014:385-388 www.amiemt-journal.com

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Abstract: This paper compares five different face detector algorithms consisting of PCA, LDA, ICA, Gabor and SVM based on the quality and accuracy of detection. This comparison have been implemented based on some data sets including ATT, AR, FERET and Yale. Based on the results of this research, the Gabor algorithm has the best accuracy and on the other hand, SVM is the best when Yale's data set is selected. In order to comparing the performance of algorithms in face determination procedure, all of them are coded and run in Matlab software. Keywords: Face detection, evaluation of algorithms. PCA, ،Gabor، SVM ،AR، FERET،ATT،Yale

1.Introduction Recently, developing the Information technology era has improved the biometric detection systems dramatically. In such systems detection occurs by voice, signature, face and etc. All of these methods have their positive and negative points. In this regard, the face detection methods have a great advantage which the image is reachable easily using some photo or video camera without any disturbing for people. However, like other methods, these algorithms may have some problems including sensitivity to light, angle of camera, facial wrinkles and etc. [1]. According to mentioned concerns, recently, researches try to reform the capability of the face detection algorithms and in this case, several researches have been implemented.( ]2[ , ]3[ , ]4[ , ]5[ , ]6[

]7[ , ]8[ , ]9[ , ]10[ ). In the current study, it is tried to compare and evaluate five well-known algorithms including PCA ]9[ ، LDA ]11[ ، ICA ]2[ ، SVM و ]12[ Gabor ]12[ based on the accuracy and effectiveness of face detection. 2. Face detector algorithms 2.1. Principle Component Analysis (PCA) This method known as the oldest among face detection algorithms and also called as Hotelling or Karhvnen-Loeve. This method focuses on the decreasing the face dimensions. PCA, first selects a set of sub vectors for sending to data set. These vectors provide an image based on detected face which called special image. The procedure of face detection using this algorithm is as follows: - Taking N initial pictures from the face - Constructing the special image based on the taken pictures - Calculating the error between the best special image to the real face

Evaluating and classification of face detector algorithms

Seyed Mohamadreza Hashemi1; Mohsen Zangian2, Mojtaba Shakeri3; Armin Pak Aghideh4 1Faculty of electricity and computer, Science and researches university, Qazvin

eng.reza2013@yahoo.com 2Faculty of electricity and computer, Science and researches university, Shahrood

mohsen_zangian@yahoo.com 3Faculty of electricity and computer, Azad university of Qazvin, Qazvin

mojtaba.shakeri@gmail.com 4Faculty of electricity and computer, Azad university of Qazvin, Qazvin

Ar.pakaghideh@qiau.ac.ir

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2.2. Linear Detection Algorithm (LDA) LDA is also known as Fisher method and like PCA, aims to decrease the number of dimensions. However, the difference of this method in comparison to PCA is that the detection procedure is an intelligent one and there is a supervising mechanism for decreasing the related errors. In this field, some clustering algorithms would be useful. [11] 2.3. Independent Component Analysis In this method, the selected vectors are plumb together and it is tried to select the borders which make the lowest variance of data for decreasing the errors. 2.4. Support Vector Machines Basically, SVM is a linear algorithm in which the main idea is to make a hyper volume as decision volume so that the Structural Risk is minimized. The procedure of this algorithm is based on the training and minimizing the error in each level periodically. [9] 2.5. Gabor wavelet This method is one of the most successful methods and is based on the multiplying graph comparisons which is obtained from Gabor wavelet responces. [13, 14] Such algorithms have some problems related to the high complexity and long running times. Basically, this method uses a general framework for face detection. Two- dimensions functions of this method highlight the dents and lumps of the face. Also, some important parts of face like eyes, lips and etc. are distinguished using these functions. Based on this determination, each face will be identified by drawing a map. 3. Datasets In this section some datasets like ATT, AR, FERET and Yale are introduced and then are used for evaluating the mentioned face detection algorithm based on the accuracy. The flexibility of face and the 3-dimension framework of it, make the face detection difficult. Many factors like light of the environment, head angle, emotions (fear, laughing and etc) and even hair type affect the determination of face's procedure. Therefore, in order to decrease these effects some datasets are used. Four well-known datasets are introduced as follows: 3.1 AR AR dataset has been provided in 1998 by Martinez and Benavente in Spain and has 116 pictures of different people.

Fig.1- The AR dataset

3.2 ATT

Applied mathematics in Engineering, Management and Technology 2 (2) 2014

This dataset called also as ORL has been published in 1992 to 1994 and contains 400 different images of 40 different people.

Fig.2 3.3. Yale This data sets take different pictures with different lighting and gesture conditions from 15 persons.

Fig. 3

3.4. FERET dataset This method was provided in 1993 to 1996 with corporation of USA military.

Fig. 4 4. Results In order to comparing the proposed algorithms, all of them were coded and run using Matlabbelow diagram shows the effectiveness of the algorithms within different datasets based on the accuracy of face determination

Fig. 5- The comparison of algorithms in different datasets

75%

80%

85%

90%

95%

100%

PCA

FERET

Applied mathematics in Engineering, Management and Technology 2 (2) 2014 M. Hashemi et al

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This dataset called also as ORL has been published in 1992 to 1994 and contains 400 different images of 40

Fig.2- ATT dataset

This data sets take different pictures with different lighting and gesture conditions from 15 persons.

Fig. 3- Yale dataset

This method was provided in 1993 to 1996 with corporation of USA military.

Fig. 4- FERET dataset

In order to comparing the proposed algorithms, all of them were coded and run using Matlab software. The below diagram shows the effectiveness of the algorithms within different datasets based on the accuracy of face

The comparison of algorithms in different datasets

LDA SVM ICA Gabor

FERET AR Yale ATT

This dataset called also as ORL has been published in 1992 to 1994 and contains 400 different images of 40

software. The below diagram shows the effectiveness of the algorithms within different datasets based on the accuracy of face

Applied mathematics in Engineering, Management and Technology 2 (2) 2014 M. Hashemi et al

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Table 1- The comparison of algorithms in different datasets PCA LDA SVM ICA Gabor

FERET 90.23%

92.83%

93.00%

96.40% 98.60%

AR 81.60%

88.30%

83.10%

94.00% 96.50%

Yale 91.00%

94.50%

97.30%

99.33% 98.70%

ATT 94.30%

98.00%

97.20%

99.14%

100.00%

5. Conclusion The aim of this paper was to introduce some well-known face determination algorithms and the related data sets and evaluating their performance based on the effectiveness and accuracy of face detecting. For this purpose, all the algorithms were coded and run in Matlab software. According to the comparisons, the Gabor method had the best performance in comparison to the other algorithms. References 1. Ashbourn, J., Biometrics: advanced identity verification. 2000: Springer-Verlag. 2. Baek, K., et al. PCA vs. ICA: A comparison on the FERET data set. in Joint Conference on Information Sciences, Durham, NC. 2002. 3. Bartlett, M.S., J.R. Movellan, and T.J. Sejnowski, Face recognition by independent component analysis. Neural Networks, IEEE Transactions on, 2002. 13(6): p. 1450-1464. 4. Belhumeur, P.N., J.P. Hespanha, and D.J. Kriegman, Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997. 19(7): p. 711-720. 5. Becker, B.C. and E.G. Ortiz. Evaluation of face recognition techniques for application to facebook. in Automatic Face & Gesture Recognition, 2008. FG'08. 8th IEEE International Conference on. 2008. IEEE. 6. Delac, K., M. Grgic, and S. Grgic, Independent comparative study of PCA, ICA, and LDA on the FERET data set. International Journal of Imaging Systems and Technology, 2005. 15(5): p. 252-260. 7. Martinez, A.M. and A.C. Kak, Pca versus lda. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2001. 23(2): p. 228-233. 8. !!! INVALID CITATION !!! 9. Vapnik, V., The nature of statistical learning theory. 1999: springer. 10. Turk, M.A. and A.P. Pentland. Face recognition using eigenfaces. in Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on. 1991. IEEE. 11. Yang, J., H. Yu, and W. Kunz. An efficient LDA algorithm for face recognition. in Proceedings of the International Conference on Automation, Robotics, and Computer Vision (ICARCV 2000). 2000. 12. Guo, G., S.Z. Li, and K. Chan. Face recognition by support vector machines. in Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. 2000. IEEE. 13. Phillips, P.J., et al., The FERET database and evaluation procedure for face-recognition algorithms. Image and vision computing, 1998. 16(5): p. 295-306. 14. Wiskott, L., et al., Face recognition by elastic bunch graph matching. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997. 19(7): p. 775-779.