Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

Adrian Sergiu Darabant, Diana Borza and Radu Danescu
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Adrian Sergiu Darabant: Computer Science Department, Babes Bolyai University, 1st Mihail Kogălniceanu Street, 400084 Cluj-Napoca, Romania
Diana Borza: Computer Science Department, Babes Bolyai University, 1st Mihail Kogălniceanu Street, 400084 Cluj-Napoca, Romania
Radu Danescu: Computer Science Department, Technical University of Cluj-Napoca, 28th George Baritiu Street, 400027 Cluj-Napoca, Romania

Mathematics, 2021, vol. 9, issue 2, 1-19

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.

Keywords: convolutional neural network; facial analysis; race recognition; race dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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