Face recognition is a challenging task in computer vision and pattern recognition. With the correspondence presents Color and Frequency Features based face recognition. The CFF method, which applies an Enhanced Fisher Model (EFM), extracts the complementary frequency features in a new hybrid color space for improving face recognition performance. A color image in the RGB color space consists of the red, green, and blue component images. The new color space, the RIQ color space, which combines the R component image of the RGB color space and the chromatic components I and Q of the YIQ color space, displays prominent capability for improving face recognition performance due to the complementary characteristics of its component images. The EFM then extracts the complementary features from the real part, the imaginary part, and the magnitude of the image in the frequency domain. The complementary features are then fused by means of concatenation at the feature level to derive similarity scores for classification. The complementary feature extraction and feature level fusion procedure applies to the I and Q component images as well. The hybrid color space improves face recognition performance significantly, and the complementary color and frequency features further improve face recognition performance. EFM method for improving face recognition performance. The KFA method achieves, better face verification rate (FVR) then the EFM method.

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The RIQ color space. Color and frequency features method (CFF) EFM and kernel fisher analysis(KFA) algorithm