Facial Recognition on the Basis of Facial Fragments
There are many articles that attempt to establish the role of different facial fragments in face recognition. Various approaches are used to estimate this role. Frequently, authors calculate the entropy corresponding to the fragment. This approach can only give approximate estimation. In this paper, we propose to use a more direct measure of the importance of different fragments for face recognition. We propose to select a recognition method and a face database and experimentally investigate the recognition rate using different fragments of faces. We present two such experiments in the paper. We selected the PCNC neural classifier as a method for face recognition and parts of the LFW (Labeled Faces in the Wild) face database as training and testing sets. The recognition rate of the best experiment is comparable with the recognition rate obtained using the whole face.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1340208Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 434
 S. Ullman, E. Sali, and M. Vidal-Naquet, “A Fragment-Based Approach to Object Representation and Classification,” C. Arcelli et al.. Eds: IWVF4, LNCS 2059, 2001, pp. 85–100.
 J. W. Tanaka, M. J. Farah, “Parts and Wholes in Face Recognition,” The Quarterly Journal of Experimental Psychology, 1993,46A (2), pp. 225-245.
 Teewoon Tan, Hong Yan, Object recognition using fractal neighbor distance: eventual convergence and recognition rates, Proceedings of the 15th International Conference on Pattern Recognition 2000 (ICPR’00), Vol.2, pp. 781-784, 2000.
 Meng Joo Er, Shiqian Wu, Juwei Lu, Hock Lye Toh, Face recognition with radial basis function (RBF) neural networks, IEEE Transactions on Neural Networks, Vol.13(3), pp. 697-710, May 2002.
 Javad Haddadnia, Majid Ahmadi, Karim Faez, An efficient method for recognition of human faces using higher orders pseudo Zernike moment invariant, Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition 2002 (FGR’02), pp. 330-335, 2002.
 Victor-Emil Neagoe, Armand-Dragos Ropot, Concurrent self-organizing maps for pattern classification, Proceedings of the First IEEE International Conference on Cognitive Informatics 2002 (ICCI’02), pp. 304-312, 2002.
 T. Phiasai, S. Arunrungrusmi, K. Chamnongthai, Face recognition system with PCA and moment invariant method, The 2001 IEEE International Symposium on Circuits and Systems 2001 (ISCAS’01), Vol.2, pp. 165-168, 2001.
 Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, Andrew D. Back, Face recognition: a convolutional neural-network approach, IEEE Transactions on Neural Networks, Vol.8(1), pp. 98-113, January 1997.
 Guodong Guo, Stan Z. Li, Kapluk Chan, Face recognition by support vector machines, Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000, pp. 196-201, 2000.
 Shang-Hung Lin, Sun-Yuan Kung, Long-Ji Lin, Face recognition/detection by probabilistic decision-based neural network, IEEE Transactions on Neural Networks, Vol.8(1), pp. 114-132, January 1997.
 Victor Brennan, Jose C. Principe, Face classification using a multiresolution principal component analysis, Proceedings of the IEEE Signal Processing Society Workshop (NNSP’98), pp. 506-515, 1998.
 Stefan Eickeler, Stefan Müller, Gerhard Rigoll, High quality face recognition in JPEG compressed images, Proceedings of the 1999 International Conference on Image Processing 1999 (ICIP’99), Vol.1, pp. 672-676, 1999.
 R. O. Duda, P. E. Hart and D. G. Stork,”Pattern classification,” Wiley, 2001.
 R. Hecht-Nielsen, “Neurocomputing, ” Addison-Wesley, 1990.
 P. D. Wasserman, “Neural Computing: theory & practice,” Van Nostrand Reinhold, 1989.
 W. Gao, B. Cao, S. Shan, X. Chen, D. Zhou, X. Zhang, and D. Zhao, “The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluation,” IEEE Trans. Systems, Man, and Cybernetics, Part A: Systems and Humans, vol. 38, no. 1, pp. 149-161, Jan. 2008.
 Z. Cruz Monterrosas, T. Baidyk, E. Kussul, A. J. Ibarra Gallardo, “Rotation Distortions for Improvement in Face Recognition with PCNC,” IEEE 3rd International Conference and Workshop on Bioinspired Intelligence, 16-18 July, 2014, Liberia, Costa Rica, pp. 50-55.
 W. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld, “Face recognition: A literature survey,” Acm Computing Surveys (CSUR), 35(4), pp. 399-458.
 Z. Lei, M. Pietikäinen, S. Li, “Learning Discriminant Face Descriptor,” IEEE Trans Pattern Analysis and Machine Intelligence, vol.36, no. 2, Feb.2014, pp. 289-302.
 FEI Face Database, Image Processing Laboratory, Department of Electrical Engineering, Centro Universitario da FEI, São Bernardo do Campo, São Paulo, Brazil, http://fei.edu.br/~cet/facedatabase.html, last accessed on 21.11.2014.
 C. E. Thomaz, G. A. Giraldi, “A New Ranking Method for Principal Components Analysis and its Application to Face Image Analysis”, Image and Vision Computing, vol. 28, no. 6, June 2010, pp. 902-913.
 FRAV3D, Universidad Rey Juan Carlos, http://www.frav.es/, accessed 05/08/2012.
 E. Kussul, T. Baidyk, C. Conde, I. Martín de Diego, and E. Cabello, “Face Recognition Improvement with Distortions of Images in Training Set”, IJCNN 2013, Dallas, Texas, USA, August 4-9, 2013, pp. 2769-2774.
 E. Kussul, T. Baydyk, “Face Recognition Using Special Neural Networks,” IJCNN 2015, Killarney, Ireland, July 12-17, 2015, pp.1-7.
 T. Baidyk, E. Kussul, Z. Cruz Monterrosas, A. J. Ibarra Gallardo, K. L. Roldán Serrato, C. Conde, A. Serrano, I. Martín de Diego, E. Cabello, “Face Recognition using a Permutation Coding Neural Classifier,” Neural Computing & Applications, 2015, DOI 10.1007/s00521-015-1913-0, 24 April 2015.
 E. Kussul, T. Baydyk, “Neural Networks and Micromechanics”, Springer-Verlag, 2010.
 G. Huang, M. Ramesh, T. Berg, “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,” University of Massachusetts, Amherst, Technical Report pp.07-49, Oct. 2007.