Face Verification Using Template Matching

Human faces are similar in structure with minor differences from person to person. These minor differences may average out while trying to synthesize the face image of a given person, or while building a model of face image in automatic face recognition. In this paper, we propose a template-matching approach for face verification, which neither synthesizes the face image nor builds a model of the face image. Template matching is performed using an edginess-based representation of the face image. The edginess-based representation of face images is computed using 1-D processing of images. An approach is proposed based on autoassociative neural network models to verify the identity of a person. The issues of pose and illumination in face verification are addressed.

[1]  Rama Chellappa,et al.  Principal components null space analysis for image and video classification , 2006, IEEE Transactions on Image Processing.

[2]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[4]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Thomas Vetter,et al.  Estimating Coloured 3D Face Models from Single Images: An Example Based Approach , 1998, ECCV.

[6]  I. Biederman,et al.  Surface versus edge-based determinants of visual recognition , 1988, Cognitive Psychology.

[7]  Yongsheng Gao,et al.  Face Recognition Using Line Edge Map , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  B. V. K. Vijaya Kumar,et al.  Spatial frequency domain image processing for biometric recognition , 2002, Proceedings. International Conference on Image Processing.

[9]  Rama Chellappa,et al.  SFS based view synthesis for robust face recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[10]  Tomaso A. Poggio,et al.  Linear Object Classes and Image Synthesis From a Single Example Image , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Hiromi T. Tanaka,et al.  Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[12]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Sethuraman Panchanathan,et al.  Framework for performance evaluation of face recognition algorithms , 2002, SPIE ITCom.

[14]  V. Bruce,et al.  The importance of ‘mass’ in line drawings of faces , 1992 .

[15]  Modesto Castrillón,et al.  Face recognition using independent component analysis and support vector machines , 2003 .

[16]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[17]  B. Yegnanarayana,et al.  Face verification using correlation filters and autoassociative neural networks , 2004, International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of.

[18]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[19]  Ralph Gross,et al.  Appearance-based face recognition and light-fields , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Jun Zhang,et al.  Pace recognition: eigenface, elastic matching, and neural nets , 1997, Proc. IEEE.

[21]  Kishore Prahallad,et al.  AANN: an alternative to GMM for pattern recognition , 2002, Neural Networks.

[22]  B. V. K. Vijaya Kumar,et al.  Significance of image representation for face verification , 2007, Signal Image Video Process..

[23]  Samy Bengio,et al.  On transforming statistical models for non-frontal face verification , 2006, Pattern Recognit..

[24]  P. Khosla,et al.  Face Verification using Correlation Filters , 2002 .

[25]  Jin Hyung Kim,et al.  Face Recognition using Support Vector Machines with Local Correlation Kernels , 2002, Int. J. Pattern Recognit. Artif. Intell..

[26]  Alex Pentland,et al.  Bayesian face recognition , 2000, Pattern Recognit..

[27]  Muhittin Gökmen,et al.  Eigenhill vs. eigenface and eigenedge , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[28]  Marios Savvides,et al.  Robust shift-invariant biometric identification from partial face images , 2004, SPIE Defense + Commercial Sensing.

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

[30]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.