Abstract
Face detection and recognition are challenging research topics in the field of robotic vision. Numerous algorithms have been proposed to solve several problems related to changes in environment and lighting conditions. In our research, we introduce a new algorithm for face detection. The proposed method uses the well-known local binary patterns(LBP) algorithm and K-means clustering for face segmentation and maximum likelihood to classify output data. This method can be summarized as a process of detecting and recognizing faces on the basis of the distribution of feature vector amplitudes on six levels, that is, three for positive vector amplitudes and three for negative amplitudes. Detection is conducted by classifying distribution values and deciding whether or not these values compose a face.
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