Facial Skin Type Detection for Race Classification using Convolutional Neural Network and Haar Cascade Method

Abstract

Every organism possesses a unique structural makeup, extending from molecules to organ systems. One such organ, the skin, is our body's largest and the most complex. Its diversity in color corresponds with various human races, although the facial skin type, an often-overlooked factor, also plays a significant role in race identification. In this research, a system was developed to recognize racial classifications from facial skin types by focusing on features in the facial T and U areas, using the Convolutional Neural Network (CNN) and Haar cascade methodologies. CNN was employed due to its ability to use the convolution process, moving a kernel across an image, multiplying it with the applied filter, and thereby generating new representative information. It is especially effective in image recognition and processing. The Haar cascade method, on the other hand, was used to outline the T and U areas on the face for the skin type detection system. The T area, known for oil detection, identifies skin types, while the U area identifies race types by forming facial patterns. This system, trained on 1670 race and 60 skin type datasets and optimized using the Adam optimizer, exhibited high accuracy levels. Upon testing with five new samples, it demonstrated an average accuracy of 98% in race detection and 97% in skin type detection.