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This paper describes the development and quantitative assessment of an approach to face detection (FD), with the application of image classification in mind. The approach adopted is a direct extension of an earlier approach by Huang [Pattern Recognition 19941. Huang's intensity based approach is found to be susceptible t o 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. The SPM allows image regions to be 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.