Face detection is the first step for automatic face recognition systems. However, detecting faces is not an easy task due to variations in factors such as pose, illumination, scale among others. Efficient face detection algorithms like the one proposed by Viola-Jones allows one to detect faces in real-time with high accuracy rates. However, this algorithm involves several stages that consume huge computation, particularly for the training process. Also, increasing input image’s size for face detection and using large training data sets for face recognition demand additional computing resources to achieve real-time processing. In this paper we present a parallel approach to perform three stages of the Viola-Jones face detection algorithm, particularly for the integral image computation, Haar-like features estimation and the evaluation of these features. Our experimental results show that our proposed approach obtains better performance than the OpenCV library implementations.
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