Facial Color Features Research Articles

The Association of Quantitative Facial Color Features with Cold Pattern in Traditional East Asian Medicine.

Introduction Facial diagnosis is a major component of the diagnostic method in traditional East Asian medicine. We investigated the association of quantitative facial color features with cold pattern using a fully automated facial color parameterization system. Methods The facial color parameters of 64 participants were obtained from digital photographs using an automatic color correction and color parameter calculation system. Cold pattern severity was evaluated using a questionnaire. Results The a⁎ values of the whole face, lower cheek, and chin were negatively associated with cold pattern score (CPS) (whole face: B = −1.048, P = 0.021; lower cheek: B = −0.494, P = 0.007; chin: B = −0.640, P = 0.031), while b⁎ value of the lower cheek was positively associated with CPS (B = 0.234, P = 0.019). The a⁎ values of the whole face were significantly correlated with specific cold pattern symptoms including cold abdomen (partial ρ = −0.354, P < 0.01) and cold sensation in the body (partial ρ = −0.255, P < 0.05). Conclusions a ⁎ values of the whole face were negatively associated with CPS, indicating that individuals with increased levels of cold pattern had paler faces. These findings suggest that objective facial diagnosis has utility for pattern identification.

Noninvasive Diabetes Mellitus Detection Using Facial Block Color With a Sparse Representation Classifier

Diabetes mellitus (DM) is gradually becoming an epidemic, affecting almost every single country. This has placed a tremendous amount of burden on governments and healthcare officials. In this paper, we propose a new noninvasive method to detect DM based on facial block color features with a sparse representation classifier (SRC). A noninvasive capture device with image correction is initially used to capture a facial image consisting of four facial blocks strategically placed around the face. Six centroids from a facial color gamut are applied to calculate the facial color features of each block. This means that a given facial block can be represented by its facial color features. For SRC, two subdictionaries, a Healthy facial color features subdictionary and DM facial color features subdictionary, are employed in the SRC process. Experimental results are shown for a dataset consisting of 142 Healthy and 284 DM samples. Using a combination of the facial blocks, the SRC can distinguish Healthy and DM classes with an average accuracy of 97.54%.