Comparison of colour transforms used in lip segmentation algorithms

Abstract

Lip segmentation is a fundamental system component in a range of applications including: automatic lip reading, emotion recognition and biometric speaker identification. The first step in lip segmentation involves applying a colour transform to enhance the contrast between the lips and surrounding skin. However, there is much debate among researchers as to the best transform for this task. As such, this article presents the most comprehensive study to date by evaluating 33 colour transforms for lip segmentation: 21 channels from seven colour space models (RGB, HSV, YCbCr, YIQ, CIEXYZ, CIELUV and CIELAB) and 12 additional colour transforms (8 of which are designed specifically for lip segmentation). The colour transform comparison is extended to determine the best transform to segment the oral cavity. Histogram intersection and Otsu’s discriminant are used to quantify and compare the transforms. Results for lip–skin segmentation validate the experimental approach, as 11 of the top 12 transforms are used for lip segmentation in the literature. The necessity of selecting the correct transform is demonstrated by an increase in segmentation accuracy of up to three times. Hue-based transforms including pseudo hue and hue domain filtering perform the best for lip–skin segmentation, with the hue component of HSV achieving the greatest accuracy of 93.85 %. The a* component of CIELAB performs the best for lip–oral cavity segmentation, while pseudo hue and the LUX transform perform reasonably well for both lip–skin segmentation and lip–oral cavity segmentation.