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.

中文翻译：

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通过机器学习识别种族—多网络，多特征研究

人脸在多学科研究中占有特权地位，因为它传达了许多信息，包括人口统计学属性（年龄，种族，性别，种族），社会信号，情感表达等。研究表明，由于种族/种族在训练数据集中的分布，生物特征识别算法会遭受“跨种族效应”的困扰-他们的算法在距离算法“起源国”较近的受试者上表现更好。本文的贡献有两个方面：（a）首先，我们通过自动搜寻Internet上属于不同种族/种族的名人图像，来收集，注释并公开一个大规模的（超过17.5万张）面部图像数据库， （b）我们在种族和种族分类问题上训练并比较了四种最先进的卷积神经网络。据我们所知，这是最大的，数据平衡的，公开可用的面部数据库，带有种族和种族信息。我们还研究了各种面部特征和图像特征对种族/民族深度学习分类方法的影响，并将所得结果与从心理学研究和拟人化研究中提取的结果进行了比较。为了确定网络对之敏感的面部特征，进行了广泛的测试。这些测试以及对人类分类问题的识别率为96.64％，证明了所提出解决方案的有效性。我们还研究了各种面部特征和图像特征对种族/民族深度学习分类方法的影响，并将所得结果与从心理学研究和拟人化研究中提取的结果进行了比较。为了确定网络对之敏感的面部特征，进行了广泛的测试。这些测试以及对人类分类问题的识别率为96.64％，证明了所提出解决方案的有效性。我们还研究了各种面部特征和图像特征对种族/民族深度学习分类方法的影响，并将所得结果与从心理学研究和拟人化研究中提取的结果进行了比较。为了确定网络对之敏感的面部特征，进行了广泛的测试。这些测试以及对人类分类问题的识别率为96.64％，证明了所提出解决方案的有效性。