Abstract
The human face holds a privileged position in multi-disciplinary research as it conveys much information—demographical attributes (age, race, gender, ethnicity), social signals, emotion expression, and so forth. Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm. The contributions of this paper are two-fold: (a) first, we gathered, annotated and made public a large-scale database of (over 175,000) facial images by automatically crawling the Internet for celebrities’ images belonging to various ethnicity/races, and (b) we trained and compared four state of the art convolutional neural networks on the problem of race and ethnicity classification. To the best of our knowledge, this is the largest, data-balanced, publicly-available face database annotated with race and ethnicity information. We also studied the impact of various face traits and image characteristics on the race/ethnicity deep learning classification methods and compared the obtained results with the ones extracted from psychological studies and anthropomorphic studies. Extensive tests were performed in order to determine the facial features to which the networks are sensitive to. These tests and a recognition rate of 96.64% on the problem of human race classification demonstrate the effectiveness of the proposed solution.
Highlights
The definition and taxonomy of human races is complex, subjective and fluid, as there are many paradigms that can be used when defining them
The Chicago Face Database [39] contains high-resolution, standardized images of 158 participants with ages between 18 and 40 years and extensive data about the subjects: race (Asian, Black, Hispanic/Latino, White), gender, facial attributes. The labels of this dataset were converted to our taxonomy using the following rules: AsianAsian, Black-African-American, Hispanic/Latino-Caucasian, White-Caucasian
The Japanese Female Facial Expression (JAFFE) [41] database contains 213 images of 7 facial expression posed by 10 Japanese female models
Summary
The definition and taxonomy of human races is complex, subjective and fluid, as there are many paradigms that can be used when defining them. The idea of human race, as known today, arose in the period of European colonization, when the colonizers took contact with local populations with different physical traits, languages, traditions and culture. People constantly classify the human beings they encounter into numerous categories based on physical traits, tradition, language and so forth These classification schemes used to create social groups and categories are crucial in understanding several concepts in human interaction, such as group polarization, social influence, social identity theory, just to name a few. This work focuses on finding the best Convolutional Neural Network (CNN)-based solution for distinguishing between ethnic groups, and on establishing the most relevant facial regions that influence the classification process. The remainder of this work is organized as follows—in Section 2 we present various anthropomorphic features that might be used to distinguish between racial groups and some theories regarding how humans perceive race.
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