Face Recognition: A Hybrid Neural Network Approach

Faces represent complex, multidimensional, meaningful visual stimuli and developing a computa- tional model for face recognition is difficult (Turk and Pentland, 1991). We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sam- pling, a self-organizing map neural network, and a convolutional neural network. The self-organizing map provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides for partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loe transform in place of the self-organizing map, and a multilayer perceptron in place of the convolu- tional network. The Karhunen-Lo` eve transform performs almost as well (5.3% error versus 3.8%). The multilayer perceptron performs very poorly (40% error versus 3.8%). The method is capable of rapid classification, requires only fast, approximate normalization and preprocessing, and consistently exhibits better classification performance than the eigenfaces approach (Turk and Pentland, 1991) on the database considered as the number of images per person in the training database is varied from 1 to 5. With 5 images per person the proposed method and eigenfaces result in 3.8% and 10.5% error respectively. The recognizer provides a measure of confidence in its output and classification error approaches zero when rejecting as few as 10% of the examples. We use a database of 400 images of 40 individuals which con- tains quite a high degree of variability in expression, pose, and facial details. We analyze computational complexity and discuss how new classes could be added to the trained recognizer.

[1]  Kunihiko Fukushima,et al.  Neocognitron: a model for visual pattern recognition , 1998 .

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

[3]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[5]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[6]  Isabelle Guyon,et al.  Comparison of classifier methods: a case study in handwritten digit recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[7]  Alex Pentland,et al.  Face recognition using view-based and modular eigenspaces , 1994, Optics & Photonics.

[8]  Garrison W. Cottrell,et al.  Non-Linear Dimensionality Reduction , 1992, NIPS.

[9]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[10]  S. P. Luttrell,et al.  Hierarchical self-organising networks , 1989 .

[11]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[12]  David K. Burton,et al.  Text-dependent speaker verification using vector quantization source coding , 1985, IEEE Trans. Acoust. Speech Signal Process..

[13]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[14]  Sunil Arya,et al.  Algorithms for fast vector quantization , 1993, [Proceedings] DCC `93: Data Compression Conference.

[15]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[16]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[17]  A. Rosenfeld,et al.  IEEE TRANSACTIONS ON SYSTEMS , MAN , AND CYBERNETICS , 2022 .

[18]  David Renshaw,et al.  Automatic face recognition , 1992 .

[19]  Rama Chellappa,et al.  Evaluation of pattern classifiers for fingerprint and OCR applications , 1994, Pattern Recognit..

[20]  Takeo Kanade,et al.  Human Face Detection in Visual Scenes , 1995, NIPS.

[21]  Ferdinando Silvestro Samaria,et al.  Face recognition using Hidden Markov Models , 1995 .

[22]  Simon Haykin,et al.  Neural network approaches to image compression , 1995, Proc. IEEE.

[23]  Ingemar J. Cox,et al.  Feature-based face recognition using mixture-distance , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Klaus Pawelzik,et al.  Quantifying the neighborhood preservation of self-organizing feature maps , 1992, IEEE Trans. Neural Networks.

[25]  Takayuki Ito,et al.  Neocognitron: A neural network model for a mechanism of visual pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[26]  Klaus Schulten,et al.  Large-Scale Simulation of a Self-organizing Neural Network: Formation of a Somatotopic Map , 1989 .

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

[28]  Tomaso A. Poggio,et al.  Learning Human Face Detection in Cluttered Scenes , 1995, CAIP.

[29]  Robert A. Jacobs,et al.  Methods For Combining Experts' Probability Assessments , 1995, Neural Computation.

[30]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[31]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[32]  Todd K. Leen,et al.  From Data Distributions to Regularization in Invariant Learning , 1995, Neural Computation.

[33]  Yingyong Qi,et al.  Signature verification using global and grid features , 1994, Pattern Recognit..

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

[35]  B. Miller,et al.  Vital signs of identity [biometrics] , 1994, IEEE Spectrum.

[36]  Yoshua Bengio,et al.  Globally trained handwritten word recognizer using spatial representation, space displacement neural networks and hidden Markov models , 1993 .

[37]  Mark S. Nixon,et al.  Extending the Feature Vector for Automatic Face Recognition , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Yann LeCun,et al.  Generalization and network design strategies , 1989 .

[39]  Harris Drucker,et al.  Boosting and Other Ensemble Methods , 1994, Neural Computation.