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Ultra Fast Grey Scale Face Detection Using Vector SIMD Programming

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3 Author(s)

This paper presents an ultra-fast detection algorithm for locating faces in grey scale videos. We first use motion detection to reduce the working area and find the approximate position of the head. Then a morphology-based technique is applied in this area to detect eye-analogue and lips-analogue regions. Next, the resulting components are used to search for potential facial features. Finally we select from the candidate triplets, the one that best represents a real face, calculating a fitness which takes into account things such as the symmetry and the proximity with the extrapolated position of the face. In order to achieve the maximal speed-up, we use the vector parallelism provided by the SIMD (simple instruction multiple data) extensions, available on most mainstream processors. The final program runs 65 times faster than the real-time. Experiments demonstrate that the success rate for single face videos reaches 85% in good conditions and can go down to 60% in harder cases. This approach can be useful in many applications, where the detection rate is not as important as the computation time, such as video face identification, or human-computer visual interfaces.

Published in:

Signal-Image Technologies and Internet-Based System, 2007. SITIS '07. Third International IEEE Conference on

Date of Conference:

16-18 Dec. 2007