DeepEthnic: Multi-Label Ethnic Classification from Face Images

Ethnic group classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning. In this paper, we propose a method of classifying an image face into an ethnic group by applying transfer learning from a previously trained classification network for large-scale data recognition. Our proposed method yields state-of- the-art success rates of 99.02%, 99.76%, 99.2%, and 96.7%, respectively, for the four ethnic groups: African, Asian, Caucasian, and Indian. 1 Introduction Ethnic classification from facial images has been studied for the past two decades with the purpose of understanding how humans perceive and determine an ethnic group from a given image. The motivation stems, for example, from the fact that (gender and) ethnicity play an important role in face-related applications, such as advertising, social insensitive-based systems, etc. Furthermore, while facial features are subject to change (due to aging, for example), ethnicity is of interest due to its invariance over time. Recent works on demographic classification are divided conceptually into appearancebased methods (using, e.g., eigenface methods, fisherface methods, etc.) and geometry-based methods (relying, e.g., on geometric parameters, such as the distance between the eyes, face width and length, nose thickness, etc.). One of the main challenges of automatic demographic classification is to avoid any "noise", such as illumination, background distortion, and a subject's pose. In this paper, we introduce a deep learning-based method, that achieves state-of-the-art results for facial image representations and classification for the four ethnic groups: African, Asian, Caucasian, and Indian. 2 Related Work 2.1 Traditional ML-Based Techniques During the past two decades, there has been enormous progress on the topic of ethnic group classification, using various classical Machine Learning methods.