The most recent advancement of both software and hardware technologies has created more demand for personalized interaction. Finding efficient facial features to represent face appearance is the most critical aspect in face recognition. Automatic facial expression recognition has become a progressive research area since it plays a vital role in the human-computer-interaction where the facial expression recognition finds huge application in areas like social interaction and social intelligence. A review of various descriptors and techniques used in facial expression recognition like the Gradient faces,local features, local binary pattern (LBP), local ternary pattern(LTP), local directional pattern (LDiP)and Local derivative pattern (LDeP) is discussed here, and out of all LDN (Local Directional number pattern a local featuredescriptor),forfaceanalysis acts efficiently which helps to encode the directional informationof theface'stexturesinquite better compact way,which produces more discriminativecodecompared to current methods. Additionally, for facial expression recognition (FER) we introduced tensor perceptual color framework (TPCF), which is based on information contains in color facial images.

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Local Pattern Tensor Approach Log-gabor Filter Face Recognition Lbp Ltp Ldn.