Performance Analysis of the Feedforward and SOM Neural Networks in the Face Recognition Problem

This paper presents a comparative study between a feedforward neural network and a SOM network. The paper also proposes the incorporation of a new spatial feature, face feature lines, FFL, to represent the faces. FFL are considered as new features based on previous studies related to face recognition tasks on newborns. Besides the face feature lines, the feature vector incorporates eigenvectors of the face image obtained with the Karhunen-Loeve transformation. A face recognition system is based on a feedforward neural network, FFBP, method. The second classification scheme uses a self organized map, SOM, architecture combined with the k-means clustering algorithm. Experiments comparing both architectures show no significant differences for the ORL database, 92% for the FFBP and 90% for the SOM. However results obtained for the Yale database, 60% for the FFBP network and 70% for the SOM, indicate a better performance with the SOM architecture

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

[2]  Alex Pentland,et al.  Interactive-time vision: face recognition as a visual behavior , 1991 .

[3]  Jian-Huang Lai,et al.  GA-fisher: a new LDA-based face recognition algorithm with selection of principal components , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Hirotsugu Kashimura,et al.  Face recognition using surface features in XYI space , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[5]  Kin-Man Lam,et al.  Face recognition under varying illumination based on a 2D face shape model , 2005, Pattern Recognit..

[6]  Meng Joo Er,et al.  High-speed face recognition based on discrete cosine transform and RBF neural networks , 2005, IEEE Transactions on Neural Networks.

[7]  Bruce Bower.  Faces of perception: It's tough to explain how people so easily tell one face from another , 2001 .

[8]  Wen Gao,et al.  Empirical comparisons of several preprocessing methods for illumination insensitive face recognition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[9]  B. V. K. Vijaya Kumar,et al.  Quaternion correlation filters for face recognition in wavelet domain , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[10]  Ralph Gross,et al.  Fisher Light-Fields for Face Recognition across Pose and Illumination , 2002, DAGM-Symposium.

[11]  Alex Pentland,et al.  Probabilistic matching for face recognition , 1998, 1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165).

[12]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[13]  Zhifeng Li,et al.  Bayesian face recognition using support vector machine and face clustering , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[14]  Yajie Tian,et al.  Handbook of face recognition , 2003 .

[15]  Josef Kittler,et al.  Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[17]  Mario I. Chacon,et al.  A Fuzzy Clustering Approach for Face Recognition Based on Face Feature Lines and Eigenvectors , 2007 .

[18]  Shuzhi Sam Ge,et al.  Face recognition by applying wavelet subband representation and kernel associative memory , 2004, IEEE Transactions on Neural Networks.

[19]  Anil K. Jain,et al.  Deformation Analysis for 3D Face Matching , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[20]  B. V. K. Vijaya Kumar,et al.  Representational oriented component analysis (ROCA) for face recognition with one sample image per training class , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[21]  Witold Pedrycz,et al.  Face recognition using a fuzzy fisherface classifier , 2005, Pattern Recognit..

[22]  Hakan Cevikalp,et al.  Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  P. C. Yuela,et al.  Wavelet-based PCA for human face recognition , 1998, 1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165).

[24]  P. Quinn,et al.  Face recognition in the newborn infant , 2001 .

[25]  Mario Ignacio Chacon Murguia,et al.  A Fuzzy Clustering Approach for Face Recognition Based on Face Feature Lines and Eigenvectors , 2007, Eng. Lett..

[26]  Mayank Vatsa,et al.  Textural feature based face recognition for single training images , 2005 .

[27]  Ming Zhang,et al.  Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees , 1996, IEEE Trans. Neural Networks.