Computerized facial diagnosis using both color and texture features

Facial diagnosis is an important diagnostic tool, and has been practiced by various traditional medicines for thousands of years. However, due to its qualitative and subjective nature, it cannot be accepted in mainstream medicine. To circumvent these issues, computerized facial diagnosis using color and texture features are extracted from facial blocks representing a facial image. A facial color gamut is constructed and six centroids located to help calculate the facial color feature vector. As for the texture feature, a 2-dimensional Gabor filter with various scales and orientations are applied. Both features are combined to diagnosis the face. The experimental results were carried out on a large dataset consisting of 142 Health and 1038 Disease samples. Using both extracted features facial gloss was first detected and employed to distinguish Health and Disease samples with an average accuracy of 99.83%. Illnesses in Disease were also separated by the analysis of each facial block. The best result was achieved using all facial blocks, which successfully classified (>71%) six illnesses.