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Ultra Fast Grey Scale Face Detection Using Vector SIMD Programming

Abstract : 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.
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Contributor : Antoine Manzanera <>
Submitted on : Friday, October 30, 2015 - 12:28:11 PM
Last modification on : Thursday, July 8, 2021 - 3:50:05 AM
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Olivier Vermeulen, Antoine Manzanera, Lionel Lacassagne. Ultra Fast Grey Scale Face Detection Using Vector SIMD Programming. 3rd International Conference on Signal-Image Technology and Internet-based Systems (SITIS'07), Dec 2007, Shanghai, China. ⟨10.1109/SITIS.2007.142⟩. ⟨hal-01222664⟩



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