Bio-inspired Hybrid Face Recognition System for Small Sample Size and Large Dataset

Face recognition has a great demands in human authentication and it becomes one of the most intensive field of biometrics research areas. In this paper, we present a bio-inspired face recognition system based on linear discriminant analysis and external clue i.e. geometrical features. The use of external clue helps to identify the face among very close match and secondly it also helps in the creation of small data set. The proposed approach is insensitive to large dataset and small sample size (SSS) and it provides 94.5% accuracy on BANCA face database. Experimental and simulation results shows that the proposed scheme has encouraging results for a practical face recognition system. The computational complexity of proposed system is more than conventional LDA due to the computation of weights during recognition and in external clue but on the other it provides significant performance gain especially on similar face database.