Face recognition: a convolutional neural-network approach

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM 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 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-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

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

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

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

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

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

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

[7]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[8]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

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

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

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

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

[13]  Yoshua Bengio,et al.  Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models , 1993, NIPS.

[14]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

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

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

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

[18]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

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

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

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

[22]  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).

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

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

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

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

[27]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

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

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

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

[31]  Norbert Krüger,et al.  Face Recognition and Gender determination , 1995 .

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

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

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

[35]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  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.

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

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