Face Detection
نویسنده
چکیده
Previous Work: Most of the previous work dealt with frontal faces. [1] used clustering to generate face and non-face prototypes. For each test pattern they calculate the distances between the pattern and the prototypes. These distances form the input to a multi-layer perceptron which classifies the pattern into a face and non-face class. In [2] they used a Support Vector Machine (SVM) with a 2nd degree polynomial kernel to classify normalized gray value patterns. A system able to deal with rotations in the image plane was proposed by [3]. It consists of two neural networks: the first estimates the orientation of the face, the second recognizes the derotated faces (frontal views). They extended this approach by using multiple networks of identical structure in the classification step [4]. An approach based on the probabilities of the occurrence of small intensity patterns (16x16 pixels) in the image of the whole face (64x64 pixels) is proposed in [5].
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