Abstract

Facial Makeup Detection Using HSV Color Space and Texture Analysis

In recent decades, 2D and 3D face analyses in digital systems have become increasingly important because of their vast applications in security systems or any digital systems that interact with humans. In fact the human face expresses many of the individual’s characteristics such as gender, ethnicity, emotion, age, beauty and health. Makeup is one of the common techniques used by people to alter the appearance of their faces. Analyzing face beauty by computer is essential to aestheticians and computer scientists.
The objective of this research is to detect makeup on images of human faces by image processing and pattern recognition techniques. Detecting changes of face, caused by cosmetics such as eye-shadow, lipstick and liquid foundation, are the targets of this study. Having a proper facial database that consists of the information related to makeup is necessary. Collecting the first facial makeup database was a valuable achievement for this research. This database consists of almost 1290 frontal pictures from 21 individuals before and after makeup. Along with the images, meta data such as ethnicity, country of origin, smoking habits, drinking habits, age, and job is provided. The uniqueness of this database stems from, first being the only database that has images of women both before and after makeup, and second because of having light-source from different angles as well as its meta data collected during the process.
Selecting the best features that lead to the best classification result is a challenging issue, since any variation in the head pose, lighting conditions and face orientation can add complexity to a proper evaluation of whether any makeup has been applied or not. In addition, the similarity of cosmetic’s color to the skin color adds another level of difficulty. In this effort, by choosing the best possible features, related to edge information, color specification and texture characteristics this problem was addressed. Because hue and saturation and intensity can be studied separately in HSV (Hue, Saturation, and Value) color space, it is selected for this application.
The proposed technique is tested on 120 selected images from our new database. A supervised learning model called SVM (Support Vector Machine) classifier is used and the accuracy obtained is 90.62% for eye-shadow detection, 93.33% for lip-stick and 52.5% for liquid foundation detection respectively. A main highlight of this technique is to specify where makeup has been applied on the face, which can be used to identify the proper makeup style for the individual. This application will be a great improvement in the aesthetic field, through which aestheticians can facilitate their work by identifying the type of makeup appropriate for each person and giving the proper suggestions to the person involved by reducing the number of trials.