A SOM Based Approach to Skin Detection with Application in Real Time Systems

A large body of human image processing techniques use skin detection as a first primitive for subsequent feature extraction. Well established methods of colour modelling, such as histograms and Gaussian mixture models have enabled the construction of suitably accurate skin detectors. However such techniques are not ideal for use in adaptive real time environments. We describe methods of skin detection using a Self-Organising Map or SOM, and show performance comparable (94% accuracy on facial images) to conventional techniques. We also introduce the AXEON Learning Processor as the basis for a hardware skin detector, and outline the potential benefits of using this system in a demanding environment, such as filtering Internet traffic, to which conventional techniques are not best suited.

[1]  Andreas Rauber,et al.  The growing hierarchical self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[2]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[3]  Samuel Kaski,et al.  Visualizing the Clusters on the Self-Organizing Map , 1994 .

[4]  James L. Crowley,et al.  Robust face tracking using color , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[5]  Shigeru Akamatsu,et al.  Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[6]  Ian Craw,et al.  Tracking and Measuring Drivers Eyes , 1995, BMVC.

[7]  RauberA.,et al.  The growing hierarchical self-organizing map , 2002 .

[8]  Ian Craw,et al.  Tracking and measuring drivers' eyes , 1995, Image Vis. Comput..

[9]  Nicholas Costen,et al.  How Should We RepresentFaces for Automatic Recognition? , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  James M. Rehg,et al.  Statistical Color Models with Application to Skin Detection , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[11]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[12]  Jorma Laaksonen,et al.  SOM_PAK: The Self-Organizing Map Program Package , 1996 .

[13]  Shaogang Gong,et al.  Tracking colour objects using adaptive mixture models , 1999, Image Vis. Comput..

[14]  Jason Brand,et al.  A comparative assessment of three approaches to pixel-level human skin-detection , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.