Hybrid features based face recognition method using Artificial Neural Network

Face recognition is a biometric tool for authentication and verification having both research and practical relevance. A facial recognition based verification system can further be deemed a computer application for automatically identifying or verifying a person in a digital image. Varied and innovative face recognition systems have been developed thus far with widely accepted algorithms. The two common approaches employed for face recognition are analytic (local features based) and holistic (global features based) approaches with acceptable success rates. In this paper, we present an intelligent hybrid features based face recognition method which combines the local and global approaches to produce a complete a robust and high success rate face recognition system. The global features are computed using principal component analysis while the local features are ascertained configuring the central moment and Eigen vectors and the standard deviation of the eyes, nose and mouth segments of the human face as the decision support entities of the Generalized Feed Forward Artificial Neural Network (GFFANN). The proposed method's correct recognition rate is over 97%.

[1]  Tomaso A. Poggio,et al.  Face recognition with support vector machines: global versus component-based approach , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[2]  Narendra Ahuja,et al.  Learning recognition and segmentation of 3-D objects from 2-D images , 1993, 1993 (4th) International Conference on Computer Vision.

[3]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[4]  K Ramesha,et al.  Dual Transform based Feature Extraction for Face Recognition , 2011 .

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

[6]  Adnan Khashman,et al.  Intelligent Face Recognition: Local Versus Global Pattern Averaging , 2006, Australian Conference on Artificial Intelligence.

[7]  A. Abbas,et al.  Expression and illumination invariant preprocessing technique for Face Recognition , 2008, 2008 International Conference on Computer Engineering & Systems.

[8]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[9]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

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

[11]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[12]  B. Noble,et al.  On certain integrals of Lipschitz-Hankel type involving products of bessel functions , 1955, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[13]  Haibin Ling,et al.  Age regression from faces using random forests , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[14]  S. A. Ranawade.  Face Recognition and Verification Using Artificial Neural Network , 2010 .

[15]  L M Patnaik,et al.  Feature Extraction based Face Recognition , Gender and Age Classification , 2010 .

[16]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[17]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[18]  Marcos Faúndez-Zanuy,et al.  Biometric security technology , 2006, IEEE Aerospace and Electronic Systems Magazine.