The International Arab Journal of Information Technology (IAJIT)

..............................
..............................
..............................


A Novel Face Recognition System by the Combination of Multiple Feature Descriptors

Face recognition system best suits several security based applications such as access control system and identity verification system. A robust system to recognise human faces, which relies upon features, is proposed in this work. Initially, the reference face is created and the features are extracted from the reference face by feature descriptors such as Local Binary Pattern (LBP), Local Vector Pattern (LVP) and Gabor Local Vector Pattern (GLVP). The extracted features are combined together and are clustered by employing cuckoo search algorithm. Finally in the testing phase, the face is recognised by Extreme Learning Machine (ELM), which differentiates faces by considering facial features. The public database ‘Faces 95’ is exploited for analysing the performance of the system. The proposed work is analysed for its performance and evaluated against existing algorithms such as Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), combination of CCA and k Nearest Neighbour (kNN) and combination of CCA and Support Vector Machine (SVM) and experimental results are satisfactory in terms of accuracy, misclassification rate, sensitivity and specificity.


[1] Abate A., Nappi M., Riccio D., and Sabatino G., “2D and 3D Face Recognition: A Survey,” Pattern Recognition Letters, vol. 28, no. 14, pp. 1885-1906, 2007.

[2] Ahonen T., Hadid A., Pietikäinen M., and Maenpaa T., “Face Recognition Based on the Appearance of Local Regions,” in Proceedings of 17th International Conference on Pattern Recognition, Cambridge, pp. 153-156, 2004.

[3] Ahonen T., Hadid A., and Pietikäinen M., “Face Recognition with Local Binary Patterns,” in Proceedings of European Conference on Computer Vision, Prague, pp. 469-481, 2004.

[4] Ahonen T., Hadid A., and Pietikainen M., “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006.

[5] Belhumeur P., Hespanha J., and Kriegman D., “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.

[6] Daugman J., “Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized By Two-Dimensional Visual Cortical Filters,” Optical Society of America Journal, vol. 2, no. 7, pp. 1160-1169, 1985.

[7] Daugman J., “Complete Discrete 2-D Gabor Transforms By Neural Networks for Image Analysis and Compression,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 36, no. 7, pp. 1169-1179, 1988.

[8] Description of the Collection of Facial Images,http://cswwww.essex.ac.uk/mv/allfaces/i ndex.html, Last Visited, 2015.

[9] Fan K. and Hung T., “A Novel Local Pattern Descriptor-Local Vector Pattern in High-Order Derivative Space for Face Recognition,” IEEE Transactions on Image Processing, vol. 23, no. 7, pp. 2877-2891, 2014.

[10] Huang D., Zhang G., Ardabilian M., Wang Y., and Chen L., “3D Face Recognition Using Distinctiveness Enhanced Facial Representations And Local Feature Hybrid Matching,” in Proceedings of 4th International Conference on Biometrics: Theory Applications and Systems, Washington, pp. 1-7, 2010.

[11] Huang Y., Xu D., and Cham T., “Face and Human Gait Recognition Using Image-To-Class Distance,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 3, pp. 431-438, 2010.

[12] Marcelja S., “Mathematical Description of the Responses of Simple Cortical Cells,” Journal of the Optical Society of America, vol. 70, no. 11, pp. 1297-1300, 1980.

[13] Nanni L. and Lumini A., “Regionboost Learning for 2D+3D Based Face Recognition,” Pattern Recognition Letters, vol. 28, no. 15, pp. 2063- 2070, 2007.

[14] Nanni L., Paci M., Brahnam S., Ghidoni S., and Menegatti E., “Virus Image Classification Using Different Texture Descriptors,” in Proceedings of 14th International Conference on Bioinformatics and Computational Biology, Las Vegas, 2013.

[15] Ojala T., Pietikäinen M., and Harwood D., “A Comparative Study of Texture Measures with Classification Based on Feature Distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996.

[16] Ojala T., Pietikainen M., and Maeenpaa T., “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002.

[17] Pietikäinen M., Ojala T., and Xu Z., “Rotation- Invariant Texture Classification Using Feature Distributions,” Pattern Recognition, vol. 33, no. 1, pp. 43-52, 2000.

[18] Shan S., Zhang W., Su Y., Chen X., and Gao W., “Ensemble of Piecewise FDA Based on Spatial Histograms of Local (Gabor) Binary Patterns for 676 The International Arab Journal of Information Technology, Vol. 16, No. 4, July 2019 Face Recognition,” in Proceedings of 18th International Conference on Pattern Recognition, Hong Kong, pp. 606-609, 2006.

[19] Singh R., Vatsa M., and Noore A., “Integrated Multilevel Image Fusion and Match Score Fusion of Visible and Infrared Face Images for Robust Face Recognition,” Pattern Recognition, vol. 41, no. 3, pp. 880-893, 2008.

[20] Tan X. and Triggs B., “Fusing Gabor and LBP Feature Sets for Kernel Based Face Recognition,” in Proceedings of Analysis and Modelling of Faces and Gestures, Rio de Janeiro, pp. 235-249, 2007.

[21] Turk M. and Pentland A., “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.

[22] Vairavan T. and Vani K., “An Efficient Age Estimation System with Facial Makeover Images Based on Key Points Selection,” The International Arab Journal of Information Technology, vol. 14, no. 1, pp. 8-17, 2017.

[23] Wiskott L., Fellous J., Kruger N., and Malsburg C., “Face Recognition By Elastic Bunch Graph Matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, 1997.

[24] Yan S., Wang H., Tang X., and Huang T., “Exploring Feature Descriptors for Face Recognition,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 629-632, 2007.

[25] Zhang B., Gao Y., Zhao S., and Liu J., “Local Derivative Pattern Versus Local Binary Pattern: Face Recognition with Higher-Order Local Pattern Descriptor,” IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 533-544, 2010.

[26] Zhang W., Shan S., Qing L., Chen X., and Gao W., “Are Gabor Phases Really Useless for Face Recognition,” Pattern Analysis and Applications, vol. 12, no. 3, pp. 301-307, 2008.

[27] Zhang W., Shan S., Gao W., Chen X., and Zhang H., “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): Anovel Non-Statistical Model for Face Representation and Recognition,” in Proceedings of 10th International Conference on Computer Vision, Beijing, pp. 786-791, 2005.

[28] Zhang W., Shan S., Zhang H., Gao W., and Chen X., “Multi-Resolution Histograms of Local Variation Patterns (MHLVP) for Robust Face Recognition,” in Proceedings of the 5th International Conference on Audio- and Video- Based Biometric Person Authentication, pp. 937- 944, 2005.

[29] Zhao G. and Pietikäinen M., “Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 915-928, 2007.

[30] Zhao W., Chellappa R., Philips P., and Rosenfeld A., “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399- 458, 2003. Nageswara Reddy is a Research Scholar of Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada. He has 9 years of Software Industry and teaching experiences for Graduate and Post Graduate engineering courses. His current research interests are Data Warehousing, Image Processing and Cloud Computing. Mohan Rao is Professor in the Department of Computer Science and Engineering, Avanthi Institute of Engineering & Technology, Narsipatnam. He did his PhD from Andhra University and his research interests include Image Processing, Wireless Networks and Information security. He has guided more than 50 M.Tech Projects and currently guiding four research scholars for Ph.D. He received many honors and he has been the member for many expert committees, member of many professional bodies and Resource person for various organizations. Chittipothula Satyanarayana is a Professor in Department of Computer science and Engineering at Jawaharlal Nehru Technological University Kakinada. He completed B. Tech and M.Tech in computer science and engineering from Andhra University, Visakha Patnam, Andhra Pradesh. He was awarded his Doctoral degree in 2008 from J.N.T. University, Hyderabad. He has 15 years of experience. His areas of interest are Image Processing, Databases, Pattern Recognition and Network Security. He published more than 30 research papers in international journals and more than 100 research papers in international conferences. He has guided 15 Research scholars are working on different areas like Image Processing, Speech Recognition, and Pattern Recognition. He guided more than 78 M.Tech Projects, 56 MCA Projects, and 36 B.Tech Projects.