Face Recognition: A Hybrid Neural Network Approach

dc.contributor.authorLawrence, Steveen_US
dc.contributor.authorGiles, C. Leeen_US
dc.contributor.authorTsoi, Ah Chungen_US
dc.contributor.authorBack, Andrew D.en_US
dc.description.abstractFaces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, 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-Loeve transform in place of the self-organizing map, and a multi-layer perceptron in place of the convolutional network. The Karhunen-Loeve transform performs almost as well (5.3% error versus 3.8%). The multi-layer 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 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 contains 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. (Also cross-referenced as UMIACS-TR-96-16)en_US
dc.format.extent2679473 bytes
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3608en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-96-16en_US
dc.titleFace Recognition: A Hybrid Neural Network Approachen_US
dc.typeTechnical Reporten_US


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