Facial Image Indexing in Multimedia Databases

Abstract:

Pictures and video sequences showing human faces are of high importance in content-based retrieval systems, and consequently face detection has been established as an important tool in the framework of many multimedia applications like indexing, scene classification and news summarisation. In this work, we combine skin colour and shape features with template matching in an efficient way for the purpose of facial image indexing. We propose an adaptive two-dimensional Gaussian model of the skin colour distribution whose parameters are re-estimated based on the current image or frame, reducing generalisation problems. Masked areas obtained from skin colour detection are processed using morphological tools and assessed using global shape features. The verification stage is based on a template matching variation providing robust detection. Facial images and video sequences are indexed according to the number of included faces, their average colour components and their scale, leading to new types of content-based retrieval criteria in query-by-example frameworks. Experimental results have shown that the proposed implementation combines efficiency, robustness and speed, and could be easily embedded in generic visual information retrieval systems or video databases.