Automatic Face Recognition System by Combining Four Individual Algorithms

The objective of face recognition involves the extraction of different features of the human face from the face image for discriminating it from other persons. It is the problem of searching a face in reference database to find the matches as a given face. The purpose is to find a face that has highest similarity with a given face in the database. Many face recognition algorithms have been developed and used as an application of access control and surveillance. For enhancing the performance and accuracy of biometric face recognition system, we use a multi-algorithmic approach, where in a combination of four different individual face recognition techniques is used. In this paper, we develop a face recognition systems based on one combination of four individual techniques namely Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), Template Matching using Correlation (Corr) and Partitioned Iterative Function System (PIFS). We fuse the scores of all of these four techniques in a single face recognition system. We perform a comparative study of face recognition rate of this face recognition system at two precision levels namely at Top-5 and at Top-10 IDs. We experiment it with a standard ORL face database. Experimentally, we find that recognition rate by PCA-DCT technique is better than by individual PCA and DCT techniques and recognition rate by PCA-DCT-Corr technique is better than the PCA-DCT technique. Overall, we find the system based on combination of all of the four individual techniques outperforms.