| Abstract: | Face detection is important in many applications in the areas of machine object recognition and pattern recognition. The kaleidoscope of applications for face detection extends across automatic image and home video content annotation, face-image stabilization and face recognition systems. Face detection is the essential front end of any face recognition system, which locates and segregates face regions from cluttered images, either obtained from video or still images. It also has numerous applications in areas like surveillance and security control systems, content-based image retrieval, video conferencing and intelligent human-computer interfaces. Most of the current face recognition systems presume that faces are readily available for processing. However, in reality, we do not get images with just faces. We need a system, which will detect, locate and segregate.
In this paper we proposed an algorithm for face detection using skin color detection (YCbCr) we also do the face recognition using fusion of methods (DCT, SIFT, LDA, HIST) (discrete cosine transform, scale invariant feature transform, linear discernment analysis, histogram) for making the recognition for the face beside showing a comparison between the methods we show that the fusion makes a good accuracy of at least %97
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