Gender classification of human faces is a trending topic and a remarkable biometric task. This research area has useful applications in several fields, such as automated border control (ABC) and forensic work. There are many approaches to gender classification in the literature; the classical approaches usually use real faces. Although good performances have been achieved, data collection remains a problem. Additionally, the privacy of individuals must be included in many existing works. These drawbacks can be overcome by using fake faces. Recently, the creation of a robust fake face corpus using machine learning has become possible. Our main contribution in the present paper is to experimentally investigate the ability of an artificial deepfake corpus to be a substitute for real corpora in facial gender classification tasks. We propose a deep learning-based approach using convolutional neural networks trained with fake faces and tested on real faces. By exploiting artificial faces, data collection obstacles are resolved for the training step, and privacy is highly preserved. Four classifiers based on popular convolutional neural network architectures were implemented. In the test phase, we used faces of real identities extracted from well-known experimental databases such as Face Recognition Technology (FERET), Faculdade de Engenharia Industrial (FEI) faces, Face Recognition and Artificial Vision (FRAV) and Labeled Faces in the Wild (LFW). The results achieved are very promising. We obtained high accuracy rates and low EER scores. They are similar to those of research works using real faces. As a result of this work, we propose a gender-labeled deepfake facial dataset containing more than 200k deepfake corpora that we will make available upon request for research purposes.

The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context.

Gender classification is an important biometric task. It has been widely studied in the literature. Face modality is the most studied aspect of human-gender classification. Moreover, the task has also been investigated in terms of different face components such as irises, ears, and the periocular region. In this paper, we aim to investigate gender classification based on the oral region. In the proposed approach, we adopt a convolutional neural network. For experimentation, we extracted the region of interest using the RetinaFace algorithm from the FFHQ faces dataset. We achieved acceptable results, surpassing those that use the mouth as a modality or facial sub-region in geometric approaches. The obtained results also proclaim the importance of the oral region as a facial part lost in the Covid-19 context when people wear facial mask. We suppose that the adaptation of existing facial data analysis solutions from the whole face is indispensable to keep-up their robustness.