Multialgorithmic Frameworks for Human Face Recognition

Abstract EN

This paper presents a critical evaluation of multialgorithmic face recognition systems for human authentication in unconstrained environment.

We propose different frameworks of multialgorithmic face recognition system combining holistic and texture methods.

Our aim is to combine the uncorrelated methods of the face recognition that supplement each other and to produce a comprehensive representation of the biometric cue to achieve optimum recognition performance.

The multialgorithmic frameworks are designed to combine different face recognition methods such as (i) Eigenfaces and local binary pattern (LBP), (ii) Fisherfaces and LBP, (iii) Eigenfaces and augmented local binary pattern (A-LBP), and (iv) Fisherfaces and A-LBP.

The matching scores of these multialgorithmic frameworks are processed using different normalization techniques whereas their performance is evaluated using different fusion strategies.

The robustness of proposed multialgorithmic frameworks of face recognition system is tested on publicly available databases, for example, AT & T (ORL) and Labeled Faces in the Wild (LFW).

The experimental results show a significant improvement in recognition accuracies of the proposed frameworks of face recognition system in comparison to their individual methods.

In particular, the performance of the multialgorithmic frameworks combining face recognition methods with the devised face recognition method such as A-LBP improves significantly.