Manhattan Penalty Based Multi-Modal System for Facial Recognition

In this paper, a new approach for multimodal biometric techniques has been proposed. The new proposed approach utilizes data fusion techniques at score level of the system algorithm. Three different feature extraction algorithms have been chosen to extract features from the face image database of the individuals. These feature extraction algorithms (Principal Component Analysis, Local Binary Pattern, and Discrete wavelets transform) are used alongside K-nearest neighbor classifier to compute different score values for the same individual. These raw score values are fused together using a newly proposed data fusion techniques based on Manhattan distance penalty weighting. The proposed Manhattan penalty weighting penalizes an individual for scoring low points and further pushes it away from the potentially winning class before data fusion is conducted. The proposed approach was implemented on two public face recognition databases; ORL face database and YALE face database. The results of the proposed approach were evaluated using the recognition rates and receiver operating characteristics of the biometric classification systems. Experimental results have shown that the proposed multimodal system performs better than the unimodal system and other multimodal systems that used different data fusion rules (e.g. Sum Rule or Product Rule). In ORL database, the recognition rate of up to 97% can be obtained using the proposed techniqu