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T-norm-based classifier and FAST features for illumination invariant face recognition

Various illumination invariant face recognition approaches have been proposed in the past. However, the issue that still remains unaddressed to a large extent is the applicability of such techniques in practical environment where computational costs and time are crucial factors in deciding the implementation and use of a face recognition system. To deal with these issues, the paper presents a technique to achieve high recognition performance using robust methodologies for feature extraction and classification which at the same time are also quick in delivering results. Features from accelerated segment test are used to identify the fiducial points. To boost the stability of the method, the conventionally used ID3 algorithm has been improved by using non-extensive entropy instead of Renyi entropy. Further, a classification method using Inner Product Classifier based on t-norms is applied which uses the errors between training features and test image features which are evaluated using the triangular/t-norms.