Face identification by means of a neural net classifier
This work addresses face recognition for security or biometric applications, but it is incremental as it builds on existing eigenfaces and neural network techniques.
The paper tackles face identification by combining eigenfaces for dimensionality reduction with a neural network classifier, achieving a recognition rate of over 87% compared to 75.5% for a classical method, even with variations in expression, details, and lighting.
This paper describes a novel face identification method that combines the eigenfaces theory with the Neural Nets. We use the eigenfaces methodology in order to reduce the dimensionality of the input image, and a neural net classifier that performs the identification process. The method presented recognizes faces in the presence of variations in facial expression, facial details and lighting conditions. A recognition rate of more than 87% has been achieved, while the classical method of Turk and Pentland achieves a 75.5%.