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Due to its wide range of use in human face-related applications, face detection has been considered one of the most important areas of research in computer vision and visual pattern recognition communities. Though current methods perform well on controlled face images, their performance degrades considerably under realistic scenarios that include pose, illumination and blur challenges as well as low-resolution images. This paper proposes an efficient approach for detecting faces in uncontrolled imaging conditions using a probabilistic framework based on Hough forests. Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time, codebooks are built upon a pool of heterogeneous local appearance features, a codebook is learned for the face appearance features that models the spatial distribution and appearance of facial parts of the human face. Extensive evaluation of the proposed method on various databases shows the usefulness of the method. We show that the suggested method improves the detection rate and accuracy outperforming the state-of-the-art methods.

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