The International Arab Journal of Information Technology (IAJIT)


A New Application for Gabor Filters in Face-Based Gender Classification

Human face is one of the most important biometrics as it contains information such as gender, race, and age. Identifying the gender based on human face images is a challenging problem that has been extensively studied due to its various relevant applications. Several approaches were used to address this problem by specifying suitable features. In this study, we present an extension of feature extraction technique based on statistical aggregation and Gabor filters. We extract statistical features from the image of a face after applying Gabor filters; subsequently, we use seven classifiers to investigate the performance of the selected features. Experiments show that the accuracy achieved using the proposed features is comparable to accuracies reported in recent studies. We used seven classifiers to investigate the performance of our proposed features. Experiments reveal that k-Nearest Neighbors algorithm (k-NN), K-Star classifier (K*), and Rotation Forest offer the best accuracies.

[1] Atkinson P. and Tatnall A., “Introduction Neural Networks in Remote Sensing,” International Journal of Remote Sensing, vol. 18, no. 4, pp. 699-709, 1997.

[2] Abdelkader C. and Griffin P., “A Local Region- Based Approach to Gender Classi. Cation from Face Images,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, pp. 52-52, 2005.

[3] Berbar M., “Three Robust Features Extraction Approaches for Facial Gender Classification,” The Visual Computer: International Journal of Computer Graphics, vol. 30, no. 1, pp. 19-31, 2014.

[4] Biswas S. and Sil J., “Advanced Computing, Networking and Informatics-Volume 1,” Springer International Publishing, 2014.

[5] Boser B., Guyon I., and Vapnik V., “A Training Algorithm for Optimal Margin Classifiers,” in Proceedings of the 5th Annual Workshop on Computational Learning Theory, Pittsburgh, pp. 144-152, 1992.

[6] Cover T. and Hart P., “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21-27, 1967.

[7] Cox I., Ghosn J., and Yianilos P., “Feature- Based Face Recognition Using Mixture- Distance,” in Proceedings CVPR IEEE 186 The International Arab Journal of Information Technology, Vol. 17, No. 2, March 2020 Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, pp. 209-216, 1996.

[8] Fawcett T., “An Introduction to ROC Analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, 2006.

[9] Fix E. and Hodges J., “Discriminatory Analysis- Nonparametric Discrimination: Consistency Properties,” International Statistical Review/Revue Internationale de Statistique, vol. 57, no. 3, pp. 238-247, 1951.

[10] Golomb B., Lawrence D., and Sejnowski T., “SEXNET: A Neural Network Identifies Sex from Human Faces,” in Proceedings of the 3rd International Conference on Neural Information Processing Systems, Denver, pp. 572-577, 1990.

[11] Gower J., “Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis,” Biometrika, vol. 53, no. 3-4, pp. 325- 338, 1966.

[12] Haghighat M., Zonouz S., Abdel-Mottaleb M., “Identification Using Encrypted Biometrics,” in Proceedings of International Conference on Computer Analysis of Images and Patterns, Berlin, pp. 440-448, 2013.

[13] Haider K., Nawaz T., Habib H., Maqsood M., and Amin T., “Gender Classification of Consumer Face Images Using Gabor Filters,” International Journal of Computer Science and Network Security, vol. 16, no. 2, pp. 46-53, 2016.

[14] Hsu C., Chang C., and Lin C., “A Practical Guide to Support Vector Classification,” Technical Report, National Taiwan University, 2003.

[15] Ignat A. and Coman M., “Gender Recognition with Gabor Filters,” in Proceedings of E-Health and Bioengineering Conference, Iasi, pp. 1-4, 2015.

[16] JafariBarani M., Faez K., and Jalili F., “Implementation of Gabor Filters Combined with Binary Features for Gender Recognition,” International Journal of Electrical and Computer Engineering, vol. 4, no. 1, pp. 108-115, 2014.

[17] Jafri R. and Arabnia H., “A Survey of Face Recognition Techniques,” Journal of Information Processing Systems, vol. 5, no. 2, pp. 41-68, 2009.

[18] Jain A., Huang J., and Fang S., “Gender Identification Using Frontal Facial Images,” in Proceedings of IEEE International Conference on Multimedia and Expo, Amsterdam, pp. 1-4, 2005.

[19] Jia S., Lansdall-Welfare T., and Cristianini N., “Gender Classification by Deep Learning on Millions of Weakly Labelled Images,” in Proceedings of IEEE 16th International Conference on Data Mining Workshops, Barcelona, pp. 462-467, 2016.

[20] Jolliffe I., Principal Component Analysis, Wiley Online Library, 2002.

[21] Kaymak Ç., Sarıcı R., and Uçar A., “Illumination Invariant Face Recognition Using Principal Component Analysis-An Overview,” Machine Vision and Mechatronics in Practice, Berlin, pp. 269-285, 2015.

[22] Khan S., Nazir M., Riaz N., and Khan M., “Optimized Features Selection Using Hybrid PSO-GA for Multi-View Gender Classification,” The International Arab Journal of Information Technology, vol. 12, no. 2, pp. 183-189, 2015.

[23] Krogh A. and Vedelsby J., “Neural Network Ensembles, Cross Validation, and Active Learning,” Advances in Neural Information Processing Systems, vol. 7, pp. 231-238, 1995.

[24] Kumari S. and Majhi B., “Classifying Gender from Faces Using Independent Components,” in Proceedings of International Conference on Computational Science, Engineering and Information Technology, Berlin, pp. 589-598, 2011.

[25] Landwehr N., Hall M., and Frank E., “Logistic Model Trees,” Machine Learning, vol. 59, no. 1- 2, pp. 161-205, 2005.

[26] Manjunath B., Chellappa R., and Malsburg C., “A Feature Based Approach to Face Recognition,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Champaign, pp. 373-378, 1992.

[27] Mansanet J., Albiol A., and Paredes R., “Local Deep Neural Networks for Gender Recognition,” Pattern Recognition Letters, vol. 70, pp. 80-86, 2016.

[28] Melville P. and Mooney R., “Constructing Diverse Classifier Ensembles Using Artificial Training Examples,” in Proceedings of the 18th International Joint Conference on Artificial Intelligence, Acapulco, pp. 505-510, 2003.

[29] Mozaffari S., Behravan H., and Akbari R., “Gender Classification Using Single Frontal Image Per Person: Combination of Appearance and Geometric Based Features,” in Proceedings of 20th International Conference on Pattern Recognition, Istanbul, pp. 1192-1195, 2010.

[30] Mu M. and Ruan Q., “Mean and Standard Deviation as Features for Palmprint Recognition Based on Gabor Filters,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 25, no. 4, pp. 491-512, 2011.

[31] Penev P. and Atick J., “Local Feature Analysis: A General Statistical Theory for Object Representation,” Journal Network: Computation in Neural Systems, vol. 7, no. 3, pp. 477-500, 1996.

[32] Phillips P., Beveridge J., Draper B., Givens G., O’Toole A., Bolme D., Dunlop J., Lui Y., Sahibzada H., and Weimer S., “The Good, the A New Application for Gabor Filters in Face-Based Gender Classification 187 Bad, and the Ugly Face Challenge Problem,” Image Vision Computing, vol. 30, no. 3, pp. 177- 185, 2012.

[33] Rodriguez J., Kuncheva L., and Alonso C., “Rotation Forest: A New Classifier Ensemble Method,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619-1630, 2006.

[34] Rumelhart D., Hinton G., and Williams R., “Learning Internal Representations by Error Propagation,” Technical Report, California University, 1985.

[35] Shah D., “The Exploration of Face Recognition Techniques,” International Journal of Application or Innovation in Engineering and Management, vol. 3, no. 2, pp. 238-346, 2014.

[36] Theodoridis S. and Koutroumbas K., Pattern Recognition, Academic Press, 2009.

[37] Tivive F. and Bouzerdoum A., “A Shunting Inhibitory Convolutional Neural Network for Gender Classification,” in Proceedings of 18th International Conference on Pattern Recognition, Hong Kong, pp. 421-424, 2006.

[38] Toews M. and Arbel T., “Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 9, pp. 1567-1581, 2009.

[39] Vapnik V. and Chervonenkis A., “On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities,” Theory of Probability and its Applications, vol. 16, no. 2, pp. 264-280, 1971.

[40] Zhao W., Chellappa R., Phillips P., and Rosenfeld A., “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399- 458, 2003.

[41] Zhao W., Krishnaswamy A., Chellappa R., Swets D., and Weng J., “Discriminant Analysis of Principal Components for Face Recognition,” in Proceedings 3rd IEEE International Conference on Automatic Face and Gesture Recognition, Nara, pp. 73-85, 1998. Ebrahim Al-Wajih, received his BSc in computer science from Hodeidah University, 2007. He received his MSc in computer science from Hodeidah University and King Fahd University of Petroleum and Minerals, 2016, Saudi Arabia. He is currently a Ph.D. student. His areas of interests are machine learning, image processing, and deep learning. Moataz Ahmed received his PhD in computer science from George Mason University in 1997. Dr. Ahmed is currently a faculty member with the Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Saudi Arabia. He also severs as Adjunct/Guest Professor in a number of universities in the US and Italy. During his career, he worked as a software architect in several software houses. His research interest includes artificial intelligence and machine learning; and automated software engineering, especially, artificial intelligence based software testing, software reuse, and cost estimation. He has supervised a number of theses and published a number of scientific papers in refereed journals and conferences in these areas.