Iraqi Kurd or Arab Male Authenticity Detection Based on Facial Feature

Abstract

As an inherent human characteristic, ethnicity plays a fundamental and critical role in biometric identification. On the other hand, the human face is the core of man’s identity, and facts such as age and race are often extrapolated automatically from the face. The objective is to utilize computer technologies to identify and categorize ethnic groups based on facial features. Convolutional neural networks (CNN), which can automatically identify underlying patterns from data, excel at learning image features and have shown state-of-the-art performance in several visual recognition challenges, such as ethnicity detection. Although the automated classification of traits such as age, gender, and ethnicity is a well-researched topic, Iraqi ethnic groupings have not yet been addressed. This study seeks to tackle the challenge of predicting the ethnicity of Iraqi male individuals based on their facial traits for the two largest ethnic groups, the Arabs, and the Kurds. Male Iraqi Kurds and Arabs were each represented by 260 image samples. The dataset underwent a diverse array of preprocessing and data enhancement techniques, including image resizing, isolation, gamma correction, and contrast stretching. Moreover, to augment the dataset and expand its diversity, various techniques such as brightness adjustment, rotation, horizontal flip, and grayscale augmentations were systematically applied, effectively increasing the overall number of images, and enriching the dataset for improved model performance. Face images of Kurds and Arabs were classified using the Faster region-based CNN (RCNN) approach of deep learning. Due to insufficient data in the dataset, we propose employing transfer learning to extract features using several pre-trained models. Specifically, we examined EfficientNetB4, ResNet-50, SqueezeNet, VGG16, and MobileNetV2, resulting in accuracies of 96.73%, 94.91%, 93.39%, 92.48%, and 90.32%, accompanied by corresponding precision values of 0.86, 0.81, 0.80, 0.70, and 0.69, respectively. It is essential to emphasize that the following inference speeds – VGG16 (4.5 ms), ResNet-50 (4.6 ms), SqueezeNet (3.8 ms), MobileNetV2 (3.7 ms), and EfficientNet-B4 (16 ms) – represent the computing times needed for each backbone. Moreover, to achieve a harmonious trade-off between precision and the time required for inference, we chose ResNet-50 as the foundational framework for our model aimed at classifying ethnicity. The study also acknowledges limitations such as the availability and diversity of the dataset. Nevertheless, despite these limitations, it provides valuable perspectives on the automated prediction of Iraqi male ethnicity through facial features, presenting potential applications in various domains. The findings contribute to the broader conversation surrounding biometric identification and ethnic categorization, underscoring the importance of ongoing research and heightened awareness of the inherent limitations associated with such studies.