在不同的光源環境下，顏色的資訊會隨著環境而改變，因此要做到穩定的膚色辨識並不容易。先前的研究指出，人類的膚色大致上可歸納於特定顏色座標系統中的一段範圍內。但是根據我們的實驗，這段範圍會隨著環境光源的改變而產生偏移，因此運用相同的標準，無法在不同的場景下做到準確的膚色辨識與切割。針對這個議題，我們提出利用影像中非色彩的資訊，找出最符合膚色的範圍。具體來說，借重臉部偵測獨立於色度的特性，我們使用臉部偵測的結果，做為調整膚色範圍的依據。實驗所得到的辨識結果，效能與效率都足以運用在以視覺為基礎的人機介面，例如手部與指尖偵測。

Robust detection of skin color is a difficult task since color information changes under different lighting conditions. Previous research indicated that human skin color is restricted to a small range of values in certain color coordinate systems. However, the ranges tend to shift with varying illumination according to our experiments. It is therefore unattainable to derive a universal standard for skin color detection and segmentation for general scenes. To address this issue, we propose to use achromatic features to identify the proper ranges of skin color in an image. Specifically, we utilize the result of face detection, which is independent of chromatic properties, to guide the process of skin color range selection. Experimental results have validated the efficacy and efficiency of the proposed methodology for vision-based human-computer interface such as hand and finger detection.

[1] B.D. Zarit, B.J. Super, Francis K. H. Quek, “Comparison of Five Color spaces in Skin Pixel Classification,” International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-time Systems, pp. 58 – 63, 1999.
[2] C. Garcia, G. Tziritas, “Face detection using quantized skin color regions merging and wavelet packet analysis,” IEEE Trans. Multimedia, vol. 1, no. 3, pp. 264 – 277, 1999.
[3] J. Cai, A. Goshtasby, “Detecting human faces in color images,” Image Vision Comput. 18, pp. 63-75, 1999.
[4] J. Letessier, F. Bérard, “Visual Tracking of Bare Fingers for Interactive Surfaces,” 17th Annual ACM Symposium on User Interface Software and Technology, pp. 119 – 122, 2004.
[5] D.A. Forsyth, and M.M. Fleck, “Identifying Nude Pictures,” Applications of Computer Vision, WACV '96., Proceedings 3rd IEEE Workshop on, IEEE, Sarasota, FL USA, pp. 103 – 108, 1996.
[6] P. Kakumanu, S. Makrogiannis, N.G. Bourbakis, “A survey of skin-color modeling and detection methods,” Pattern Recognition 40(3), pp.1106-1122, 2007.
[7] K. Sobottka, I. Pitas, “A novel method for automatic face segmentation, facial feature extraction and tracking, Signal Process,” Image Communication. 12, pp. 263-281, 1998.
[8] D. Chai, K.N. Ngan, “Face segmentation using skin-color map in videophone applications,” IEEE Trans. Circuits Syst. Video Technol. 9, 1999.

[9] S. Kawato, J. Ohya, “Automatic Skin-color Distribution Extraction for Face Detection and Tracking,” ICSP2000: The 5th Int. Conf. on Signal Processing, pp.1415-1418, 21–25, 2000.
[10] S. L. Phung, A. Bouzerdoum and D. Chai, “Skin segmentation using color pixel classification: analysis and comparison,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 1, 2005.
[11] P. Viola and M. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision, Vol. 57, Issue 2, pp.137-154, 2004.
[12] C.W. Yeh and W.H. Liao, “Magic Mirror: Design Issues of Smart Display Devices,” 18th IPPR Conference on Computer Vision, Graphics, and Image Processing, pp. 59 – 63, 2005.