Abstract
Face recognition system has been evolving as a convenient biometric mode for human authentication. Face recognition is the problem of searching a face in the reference database to find a face that matches a given face. The purpose is to find a face in the database, which has highest similarity with a given face. The task of face recognition involves the extraction of different features of the human face from the face image for discriminating it from other persons. Many face recognition algorithms have been developed and have been commercialized for applications such as 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 two different individual face recognition techniques is used. We develop six face recognition systems based on the six combinations of four individual techniques namely Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), Template Matching using Correlation and Partitioned Iterative Function System (PIFS). We fuse the scores of two of these four techniques in a single face recognition system. We pperform a comparative study of recognition rate of these face recognition systems at two precision levels namely at top- 5 and at top-10. We experiment with a standard database called ORL face database. Experimentally, we find that each of these six systems perform well in comparison to the corresponding individual techniques. Overall, the system based on combination of PCA and DCT is giving the best performance among these six systems.
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Zakariya, S.M., Ali, R., Lone, M.A. (2011). Automatic Face Recognition Using Multi-Algorithmic Approaches. In: Aluru, S., et al. Contemporary Computing. IC3 2011. Communications in Computer and Information Science, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22606-9_49
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DOI: https://doi.org/10.1007/978-3-642-22606-9_49
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