This paper presents a method for the detection of faces (via skin regions) in images where faces may be low-resolution and no assumptions are made about fine facial features being visible. This type of data is challenging because changes in appearance of skin regions occur due to changes in both lighting and resolution. We present a non-parametric classification scheme based on a histogram similarity measure. By comparing performance of commonly-used colour-spaces we find that the YIQ colour space with 16 histogram bins (in both 1 and 2 dimensions) gives the most accurate performance over a wide range of imaging conditions for non-parametric skin classification. We demonstrate better performance of the non-parametric approach vs. colour thresholding and a Gaussian classifier. Face detection is subsequently achieved via a simple aspect-ratio and we show results from indoor and outdoor scenes. © 2009 SPIE-IS&T.
|Journal||Proceedings of SPIE - the International Society for Optical Engineering|
|Publication status||Published - 2009|
|Event||Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques - San Jose, CA, United States|
Duration: 19 Jan 2009 → 20 Jan 2009