C3 Effective features inspired from Ventral and dorsal stream of visual cortex for view independent face recognition
Abstract
This paper presents a model for view independent recognition using features biologically inspired from dorsal and ventral stream of visual cortex. The presented model is based on the C3 features inspired from Ventral stream and Itti's visual attention model inspired from the dorsal stream of visual cortex. The C3 features, which are based on the higher layer of the HMAX of the ventral stream of visual cortex, are modified to extract important features from faces in various viewpoints. By Itti's visual attention model, visual attention points are detected from faces in various views of faces. Effective features are extracted from these visual attention points and the view independent C3 effective features (C3EFs) are created from faces. These C3EFs are used to distinct multi-classes of different subjects in various views of faces. The presented model is tested using FERET face datasets with faces in various views of faces, and compared with C2 features (C2SMFs) and C3 features of standard HMAX model (C3SMFs). The results illustrated that our presented view independent face recognition model has high accuracy and speed in comparison with standard model features, and can recognize faces in various views by 97% accuracy.
Keywords
Full Text:
PDFReferences
[1] Z. Fan, and B. Lu, "Multi-View Face Recognition with Min-Max Modular Support Vector Machines", in Proceedings of the 1st International Conference on Advances in Natural Computation, Changsha, China, 2007, pp. 396-399.
[2] T.K. Kim, and J. Kittler, "Design and Fusion of Pose-Invariant Face-Identification Experts", IEEE Trans. Circuits and Systems for Video Technology, Vol. 16, No.9, 2006, pp. 1096–1106.
[3] K. R Singh., M. A. Zaveri, and M. M. Raghuwanshi M. M., "Illumination and Pose Invariant Face Recognition: A technical Review", IJCISIM, Vol. 2, 2010, pp. 029–038.
[4] R.H. Wurtz, and E.R. Kandel, Central visual pathways. In: E.R .Kandel, J.H. Schwartz, T.M. Jessell, editors. Principles of neural science. 4th ed. New York: McGraw-Hill, p. 523–547, 2000.
[5] E. Kobatake, and K. Tanaka, "Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex", Journal of Neurophysiology, Vol.71, No.3, 1994, pp. 856–867.
[6] Ungerleider L. G., J. V. Haxby, "‘‘What’’ and ‘‘where’’ in the human brain", Current Opinion in Neurobiology, Vol.4, No.2, pp. 157-165, 1994.
[7] L.G. Ungerleider, and L. Pessoa, "What and where pathways", Scholarpedia, Vol. 3, No.11, 2008, pp. 5342.
[8] J.M. Brown, "Visual streams and shifting attention", Progress in Brain Research, Vol. 176, 2009, pp. 47-63.
[9] R. Farivar, "Dorsal–ventral integration in object recognition", Brain Research Reviews, Vol.61, No.2, , 2009, pp. 144–153.
[10] M. Riesenhuber, and T. Poggio, "Hierarchical Models of Object Recognition in Cortex", nature neuroscience, Vol. 2, No.11, 1999, pp. 1019–1025.
[11] M. Riesenhuber, and T. Poggio, "Models of object recognition," nature neuroscience, Vol. 3, No. Suppl, 2000, pp. 1199–1204.
[12] T. Serre, and M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, and T. Poggio, A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex, Technical Report, MIT, Massa- chusetts, USA., 2005.
[13] T. Serre, and L. Wolf, S. Bileschi, S. Bileschi, M. Riesenhuber, "Robust object recognition with cortex-like mechanisms", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.29, No. 3, 2007, pp. 411–426.
[14] J. Lai, and W.X. Wang, "Face recognition using cortex mechanism and svm", in 1st international conference intelligent robotics and applications, Wuhan, 2008, pp. 625–632.
[15] E. Meyers, and L. Wolf, "Using Biologically Inspired Features for Face Processing", International Journal of Computer Vision, Vol. 76, 2008 ,pp. 93–104.
[16] P. Pramod Kumar, and P. Vadakkepat, "Hand posture and face recognition using a fuzzy-rough approach", International Journal of Humanoid Robotics, Vol. 7, No. 3, 2010, pp. 331–356.
[17] S. Dura-Bernal, T. Wennekers, and S. L. Denham, "Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation", PLoS ONE, Vol. 7, No. 11, 2012.
[18] J. Mutch, and D.G. Lowe, "Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields", International Journal of Computer Vision, Vol. 80, No. 1, 2008, pp. 45-57.
[19] C. Thériault, N. Thome, M. Cord, "Extended Coding and Pooling in the HMAX Model", IEEE Transactions on Image Processing, Vol. 22, No. 2, 2013, pp. 764 – 777.
[20] J. Z. Leibo, J. Mutch, and T. Poggio, "Why the Brain Separates Face Recognition from Object Recognition", in In Advances in Neural Information Processing Systems (NIPS), Granada, Spain, 2011.
[21] J. Han, and K.N. Ngan, "Unsupervised extraction of visual attention objects in color images", IEEE Transactions on Circuits and Systems for Video Technology, Vol. 16, No. 1, 2006, pp. 141-145.
[22] L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual-attention for rapid scene analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 11, 1998, pp. 1254–1259.
[23] L. Itti, and C. Koch, "Computational modeling of visual attention", Nature Reviews Neuroscience, Vol. 2, No. 3, 2001, pp. 194-203.
[24] D. Walther, and C. Koch, "Modeling attention to salient proto-objects", Neural Networks, Vol. 19, No. 9, 2006, pp. 1395–1407.
[25] F. Kano, and M. Tomonaga, "Face scanning in chimpanzees and humans: Continuity and discontinuity", Animal Behaviour, Vol. 79, 2010, pp. 227.
[26] P.J. Phillips, H. Wechsler, J. Huang, P. Rauss, "The FERET database and evaluation procedure for face recognition algorithms", Image and Vision Computing, Vol. 16, No. 5, 1998, pp. 295-306.
[27] C.C. Chang, C.J. Lin, LIBSVM: A library for support vector machines, Available at http://www.csie.ntu.edu.tw/cjlin/libsvm.