Orientation Histograms for Face Recognition

In this paper we present a method to recognize human faces based on histograms of local orientation. Orientation histograms were used as input feature vectors for a k-nearest neigbour classifier. We present a method to calculate orientation histograms of n×n subimages partitioning the 2D-camera image with the segmented face. Numerical experiments have been made utilizing the Olivetti Research Laboratory (ORL) database containing 400 images of 40 subjects. Remarkable recognition rates of 98% to 99% were achieved with this extremely simple approach.

[1]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

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

[3]  David G. Stork,et al.  Pattern Classification , 1973 .

[4]  Bartlett W. Mel.  SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.

[5]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[6]  Jiri Matas,et al.  Support vector machines for face authentication , 2002, Image Vis. Comput..

[7]  William T. Freeman,et al.  Orientation Histograms for Hand Gesture Recognition , 1995 .

[8]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

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

[10]  Günther Palm,et al.  Object Classification Using Simple, Colour Based Visual Attention and a Hierarchical Neural Network for Neuro-symbolic Integration , 1999, KI.

[11]  Wolfram Burgard,et al.  KI-99: Advances in Artificial Intelligence , 2003, Lecture Notes in Computer Science.

[12]  Cheng-Lin Liu,et al.  Directional pattern matching for character recognition revisited , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[13]  Cheng-Lin Liu,et al.  Handwritten digit recognition: benchmarking of state-of-the-art techniques , 2003, Pattern Recognit..

[14]  Bernd Jähne,et al.  Practical handbook on image processing for scientific applications , 1997 .

[15]  Massimiliano Pontil,et al.  People Recognition in Image Sequences by Supervised Learning , 2000 .