Face Recognition Hybrid Fisher Linear Discriminant Analysis

M.Z. Chua, K.S. Subari, M.F.A. Fauzi, and M.H.L. Abdullah (Malaysia)


Computer Vision, Face Recognition, Fisher Linear Dis criminant Analysis, Hybrid Approach Face Recognition.


Face recognition algorithms using the Fisher linear dis criminant (FLD) approach currently suffers low general ization issues when tested against images that differ signif icantly from those contained in the training set or gallery. In this paper, a Hybrid-FLD (H-FLD) approach is proposed to mitigate this disadvantage. This technique divides the face into smaller sub-sections comprising of major facial components such as eyes, mouth and nose, consequently exploiting the global and local approaches of face recogni tion algorithms. The proposed method was tested on the ORL face database. Our experiment shows that H-FLD was able to achieve 99.2 % success rate for correctly au thenticating a probe from the gallery, an achievement 16.8 % higher than the conventional FLD approach.

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