Human Face Recognition

Correlation Pattern Recognition for face Recognition

Correlation Pattern Recognition for Face Recognition


                  Machine recognition of human faces from still images and video frames is an active research area due to the increasing demand for authentication (1 : 1 matching) and identification (1 : N matching) in commercial and government applications. Despite the research advances over the years, face recognition (FR) is still a highly challenging task in practice due to the large variability in the facial appearance due to expressions, pose, illumination variations, and aging. Many well-known FR algorithms such asEigenfaces and Fisherfaces work in the image domain. However, any image is also completely described by its two-dimensional (2-D) Fourier transform (FT) andthere are advantages (mainly shift-invariance, graceful degradation, and closed form solutions) to working in the spatial frequency domain. Our main objective of is to illustrate the benefits of using spatial frequency domain representation for FR while indicating its potential limitations. 

                  Much research exists in the use of spatial frequency domain methods for automatic target recognition (ATR) where the goal is to locate and classify various targets in a scene. The targets can exhibit significant variability (due to range variability, rotations, occlusions, etc.), and spatial frequency domain methods (also known as correlation filters or correlation pattern recognition) have been used successfully to deal with the resulting appearance variability. In this paper, we show that correlation filters are useful for FR.


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