A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach

• Main Authors: Norah A. Al-Humaidan And, Master Prince
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Arab; convolutional neural network (CNN); deep learning; deep clustering; ethnicity
• Online Access: https://ieeexplore.ieee.org/document/9387317/

Human face and facial features gain a lot of attention from researchers and are considered as one of the most popular topics recently. Features and information extracted from a person are known as soft biometric, they have been used to improve the recognition performance and enhance the search engine for face images, which can be further applied in various fields such as law enforcement, surveillance videos, advertisement, and social media profiling. By observing relevant studies in the field, we noted a lack of mention of the Arab world and an absence of Arab dataset as well. Therefore, our aim in this paper is to create an Arab dataset with proper labeling of Arab sub-ethnic groups, then classify these labels using deep learning approaches. Arab image dataset that was created consists of three labels: Gulf Cooperation Council countries (GCC), the Levant, and Egyptian. Two types of learning were used to solve the problem. The first type is supervised deep learning (classification); a Convolutional Neural Network (CNN) pre-trained model has been used as CNN models achieved state of art results in computer vision classification problems. The second type is unsupervised deep learning (deep clustering). The aim of using unsupervised learning is to explore the ability of such models in classifying ethnicities. To our knowledge, this is the first time deep clustering is used for ethnicity classification problems. For this, three methods were chosen. The best result of training a pre-trained CNN on the full Arab dataset then evaluating on a different dataset was 56.97%, and 52.12% when Arab dataset labels were balanced. The methods of deep clustering were applied on different datasets, showed an ACC from 32% to 59%, and NMI and ARI result from zero to 0.2714 and 0.2543 respectively.