An atlas of functions
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
A systematic method for exploring contour segment descriptions
Cybernetics and Systems - Special issue: Eurocast 1991 international workshop on computer aided systems theory
Representation of similarity in three-dimensional object discrimination
Neural Computation
Flexible images: matching and recognition using learned deformations
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments with a featureless approach to pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
Representation and recognition in vision
Representation and recognition in vision
Face Recognition Using the Discrete Cosine Transform
International Journal of Computer Vision - Special issue: Research at McGill University
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Retinal vision applied to facial features detection and face authentication
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Primal sketch feature extraction from a log-polar image
Pattern Recognition Letters - Special issue: Sibgrapi 2001
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Memory-Based Face Recognition for Visitor Identification
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Stabilizing Classifiers for Very Small Sample Sizes
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
A wavelet subspace method for real-time face tracking
Real-Time Imaging
A face recognition system based on local feature analysis
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Face verification in polar frequency domain: a biologically motivated approach
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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A novel biologically motivated face-recognition algorithm based on polar frequency is presented. Polar frequency descriptors are extracted from face images by Fourier--Bessel transform (FBT). Next, the Euclidean distance between all images is computed and each image is now represented by its dissimilarity to the other images. A pseudo-Fisher linear discriminant was built on this dissimilarity space. The performance of discrete Fourier transform (DFT) descriptors and a combination of both feature types was also evaluated. The algorithms were tested on a 40- and 1196-subjects face database (ORL and FERET, respectively). With five images per subject in the training and test datasets, error rate on the ORL database was 3.8, 1.25, and 0.2% for the FBT, DFT, and the combined classifier, respectively, as compared to 2.6% achieved by the best previous algorithm. The most informative polar frequency features were concentrated at low-to-medium angular frequencies coupled to low radial frequencies. On the FERET database, where an affine normalization preprocessing was applied, the FBT algorithm outperformed only the PCA in a rank recognition test. However, it achieved performance comparable to state-of-the-art methods when evaluated by verification tests. These results indicate the high informative value of the polar frequency content of face images in relation to recognition and verification tasks and that the Cartesian frequency content can complement information about the subjects' identity, but possibly only when the images are not prenormalized. Possible implications for human face recognition are discussed.