Detecting Facial Features by Heteroassociative Memory Neural Network Utilizing Facial Statistics
In this paper, we present an efficient algorithm of extracting the multiple facial features such as eyes, nose, and mouth. The face candidates are first obtained based on skin-color filtering inYC b C r color domain and skin-temperature values and then the elliptic measures are applied to extract a true face candidate and its boundary. A Sobel edge mask is performed and consequently horizontal projection operation is applied to locate the eyes referring to the maximum horizontal projection value in Y component. Once two eyes are located, the distance that crosses the center of eyes and extends to the face boundary, D 1 is determined. A heteroassociative memory neural network model is utilized to find the facial features. An input neuron vector X accepts D 1 and the output neurons vector Y maps it to the facial features such as eyes, nose and mouth.
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