Face detection in color images using fusion of the chrominance and luminance channel decisions

We propose a new face detection model based on the fusion of the color and luminance channel decisions. Each of the two detection branches has its own technique of finding face candidates. We have also investigated the decision improvement of skin detection over the color channel by applying the conversion from the conventional RGB space into the 3D uncorrelated color space (UCS). One evaluates the performances of the proposed model by comparison to several standard color spaces.

[1]  Ioannis Pitas,et al.  Face localization and facial feature extraction based on shape and color information , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[2]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Federico Girosi,et al.  Reducing the run-time complexity of Support Vector Machines , 1999 .

[4]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Bernhard Schölkopf,et al.  Face Detection - Efficient and Rank Deficient , 2004, NIPS.

[6]  A. Martínez,et al.  The AR face databasae , 1998 .

[7]  B. Menser,et al.  Face detection in color images using principal components analysis , 1999 .

[8]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[9]  Victor-Emil Neagoe,et al.  Decorrelation of the Color Space, Feature/Decision Fusion, and Concurrent Neural Classifiers for Color Pattern Recognition , 2008, IPCV.

[10]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[12]  Abdesselam Bouzerdoum,et al.  Skin segmentation using color pixel classification: analysis and comparison , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Victor-Emil Neagoe,et al.  An Optimum 2D Color Space for Pattern Recognition , 2006, IPCV.