Skin probability map and its use in face detection

This paper is in two parts. The first part quantatively assesses an approach to skin segmentation. The second part describes the development and quantitative assessment of an approach to face detection (FD), with the application of content-based image retrieval in mind. Skin detection is introduced as a front-end to an earlier approach to FD by Huang (1994). The baseline approach searches grey scale images only, and is found to be susceptible to variations in lighting conditions and complex backgrounds. It is hypothesised that by integrating colour information into Huang's approach, the number of false faces can be reduced. A skin probability map (SPM) is generated from a large quantity of labeled data (530 images containing faces and 714 images that do not) and is used to pre-process colour test images. Image regions are then ranked in terms of their skin content, thus removing improbable face regions. The performance improvements are shown in terms of false acceptance (FA) and false rejection (FR) scores. As a front-end to Huang's approach, the benefits of skin segmentation can be seen by a reduction in the FA score from 79% to 15% with a negligible impact on FR.