Face recognition using HMAX method for feature extraction and support vector machine classifier

Whereas features extraction is an important phase in face recognition, we intend to use a new features extraction technique which is robust with respect to rotate and scale variant, in this paper. Therefore we use original HMAX and new HMAX model which is motivated by a quantitative model of visual cortex. The identification process can be divided into the following stages: capturing the image, preprocessing image, extracting the face from image and normalizing it, and then extracting features, finally, we used the K-nearest neighbor (KNN) and support vector machine (SVM) as classifiers. The ORL database is exploited to test our approach. The experimental results showed the effectiveness of the system in terms of the recognition rate.

[1]  Tomaso Poggio,et al.  Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines , 2006 .

[2]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[5]  T. Gawne,et al.  Responses of primate visual cortical V4 neurons to simultaneously presented stimuli. , 2002, Journal of neurophysiology.

[6]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  D. G. Albrecht,et al.  Striate cortex of monkey and cat: contrast response function. , 1982, Journal of neurophysiology.

[8]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Q. M. Jonathan Wu,et al.  Curvelet based face recognition via dimension reduction , 2009, Signal Process..

[10]  D H HUBEL,et al.  RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.

[11]  Xuelong Li,et al.  Binary Two-Dimensional PCA , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Martin A. Giese,et al.  Biophysiologically Plausible Implementations of the Maximum Operation , 2002, Neural Computation.

[13]  Pong C. Yuen,et al.  Human face recognition using PCA on wavelet subband , 2000, J. Electronic Imaging.

[14]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Tomaso Poggio,et al.  Generalization in vision and motor control , 2004, Nature.

[16]  Thomas Serre,et al.  Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex , 2004 .

[17]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[18]  N. Logothetis,et al.  Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.