1997/01/01 by S. Lawrence, Sandra Lawrence, C.L. Giles +4 · 3,115 citations
Computer Science · Engineering · #Artificial intelligence #Artificial neural network #Computer science #Computer vision #Convolutional neural network #Dimensionality reduction #Face and Expression Recognition #Facial recognition system #Feature extraction #Image Retrieval and Classification Techniques #Multilayer perceptron #Pattern recognition (psychology) #Remote-Sensing Image Classification
paper · doi:10.1109/72.554195
published in IEEE Transactions on Neural Networks 8(1), 98-113 (Institute of Electrical and Electronics Engineers)
openalex publication_date 1997/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
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.