Face Recognition Using Face Unit Radial Basis Function

Face Recognition Using Face Unit Radial Basis Function Networks Ben S. Feinstein Harvey Mudd College December 1999

Original Project Proposal • Try to reproduce published results for RBF neural nets performing face-recognition.

Recap of RBF Networks • Neuron responses are “locally-tuned” or “selective” for some range of input space. • Biologically plausible: Cochlear stereocilia cells in human ear exhibit locally-tuned response to frequency. • Contains 1 hidden layer of radial neurons, usually gaussian functions. Hidden layer output fed to output layer of linear neurons.

Recap of RBF Networks (2)

Face Unit Network Architecture • First proposed in June 1995 by Dr. A. J. Howell, School of Cognitive and Computing Sciences, Univ. of Sussex, UK. • A face unit is structured to recognize only one person, using hybrid RBF architecture. • Network has two linear outputs, one indicating a positive ID of the person, the other a negative ID.

Face Unit Architecture (2) • An p+a face unit network has p radial neurons linked to the + output, and a neurons linked to the - output. • Challenges – Bitmap faces are big dimensionally – How to reduce dimensionality of problem, extracting only the relevant information?

Gabor Wavelet Analysis • Answer: Use 2 D Gabor wavelets, class of orientation and position selective functions. • In this case, reduces dim from |10, 000| (100 x 100 pixel sample) to |126|. • Biologically plausible: Cells in visual cortex respond selectively to stimulation that is both local in retinal position and local in angle of orientation.

Approach to Problem • Sample data – 10 people x 10 poses of each person ranging from 0° (head-on) to 90° (side profile) = 100 sample images – All images 384 x 287 pixel grayscale Sun rasterfiles, courtesy of Univ. of Sussex face database. – 5 men and 5 women in sample set, mostly Caucasian.

Approach to Problem (2) • Example of images for 1 person. . .

Approach to Problem (3) • Preprocessing – Used a 100 x 100 pixel window around pixel at tip of the nose. • Wrote Nose. Picker Java app to display images and save manually clicked nose coordinates. – Used Gabor orientations (0°, 60°, 120°) with sine and cosine masks = 6 functions. – Calculated the 6 Gabor masks on 99 x 99, 4 51 x 51, and 16 25 x 25 pixel subsamples = |126|.

Approach to Problem (4) • Preprocessing – Sampling windows and orientations. . .

Approach to Problem (5) • Network Setup/Training – All input vectors were unit normalized, and the unit normalized gaussian function was used. – For each p+a face unit network, fixed set of p poses were used to center the + neurons. – For each + neuron, the nearest p/a unique negative input vectors are used to center p/a neurons.

Approach to Problem (6) • Network Setup/Training, Cont. – Setting appropriate widths for + and - neurons remains a problem. – Linear output weights are computed by finding the pseudoinverse of the matrix of hidden neuron outputs for each input, A. • Since we want Aw = d => w = A-1 d • Used singular value decomposition method to approximate A-1 since A is singular.

Approach to Problem (7) • Network Setup/Training, Cont. – Advantages are instantaneous “training”, since training is no longer iterative process, unlike gradient descent. – Only need to find pseudoinverse and perform matrix vector multiplication to calculate linear output weight vector.

Results • Currently have tested 3+6 and 6+12 networks. • Selection of neuron widths remains a problem, with manual tweaking necessary for good results. • 3+6 performs about like a random classifier.

Results (2) • 6+12 network performed better (see below) – Min correct – 37. 8% – Max correct – 95. 1% – Avg correct – 72. 6% Min pro 0 Max pro 100% Avg. pro 55. 0% Min anti 37. 2% Max anti 98. 7% Avg. ant 73. 5%

Results (3) • Compare with Dr. Howell (see below) – Avg correct – 89% – – Min pro 50% Max pro 100% Min anti 83 Max anti 100% • Better, however Dr. Howell used a more complex preprocessing scheme, yielding input vectors of |510|.

Future Work • Devise algorithm to choose appropriate neuron widths for + and - neurons or experiment with other radial basis functions that don’t need widths, such as the thin spline. • Implement a network of face units, whose output will indicate a face’s identity instead of just an affirmative or negative response.

Future Work (2) • Implement a confidence threshold to automatically discard low-confidence results. • Expand Gabor preprocessing scheme to yield more coefficients.

What Code Was Written? • Wrote C++ RBFNet class and rbf app to implement RBF net with n dimensional input and 1 linear output neuron. – Uses k-means clustering, global first nearest neighbor heuristic, and gradient descent. • Wrote C++ Face. Unit class and face_net app to implement a scalable face unit network.

What Code Was Written? (2) • Wrote Java app to display images and save manually clicked nose coordinates. • Wrote C++ program to perform image sampling and Gabor wavelet preprocessing. • Wrote perl scripts to generate input files. Hope to soon have perl script to automatically run input files and compile performance results.

Acknowledgments • Dr. A. J. Howell, School of Cognitive and Computing Sciences, Univ. of Sussex, UK. – Provided Gabor data and sample face images. • Dr. Robert Oostenveld, Dept. of Medical Physics and Clinical Neurophysiology, University Nijmegen, The Netherlands. – Provided C routine for SVD pseudoinverse calculation.

Acknowledgments (2) • Numerical Recipies Software, Numerical Recipies in C: The Art of Scientific Computing. – Used their published singular value decomposition routine in C. • And last, but not least… Prof. Keller – Invaluable guidance and advice regarding this project.