Three information set-based feature types for the recognition of faces

Abstract

This paper presents three feature types based on the concept of information set for face recognition. The first set of features includes sigmoid and energy features. The second set of features includes two features, viz. effective information set features-I and features-II and their combinations using t-norms, s-norms of Hamacher and Yager. The third set of features includes two hybrid features called Gabor-information set features and wavelet-information set features. These are extracted by applying the information set concept on the responses of Gabor filter bank and on the approximation components of the wavelet decompositions of the original face images. In addition to these two hybrid features, Hanman filter is developed by combining the information set and cosine function. The performance of all three types involving a total of seven features, two from the first type, two from the second type and three from the third type is evaluated on AT&T database using the proposed Hanman classifier formulated from the conditional entropy function and SVM. The effectiveness of these features is also demonstrated on another database called Indian Face database of IIT Kanpur having wide pose variations. The Hanman filter is shown to have consistent performance on these two databases.