Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks





Gender classification, face biometrics, facial occlusions, mask-wearing, convolutional neural networks, explainable artifical intelligence


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.


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Author Biographies

Mohamed Oulad-Kaddour, École Nationale Supérieure d'Informatique

Mohamed Oulad-Kaddour Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supèrieure d’Informatique, Oued-Smar, Algiers, Algeria Mohamed Oulad-Kaddour received the engineering and magister degrees from Ecole Nationale Supèrieure d’Informatique (ESI), Algiers, in 2011 and 2015, respectively, where he is currently pursuing the Ph.D. degree. He is an Assistant Professor with Ecole Nationale Supèrieure des Travaux Publics (ENSTP), Algiers. He is writing the Ph.D. in collaboration with the Face Recognition and Artificial Vision (FRAV) Research Group, Universidad Rey Juan Carlos, Madrid. His research interests include image classification, biometric categorization, machine learning, and image processing.

Daniel Palacios-Alonso, King Juan Carlos University

Daniel Palacios-Alonso Escuela Tècnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Campus de Mostoles, Madrid, Spain Daniel Palacios-Alonso was born in Madrid, Spain. He received the B.S. and M.S. degrees in computer science and the Ph.D. degree in advanced computation from Universidad Politécnica de Madrid (UPM), in 2009 and 2017, respectively. He was a Team Leader at a technological consulting firm for five years. Since 2013, he has been a member of the Neuromrphic Speech Processing Laboratory, Center for Biomedical Technology. He is currently an Associate Professor with Universidad Rey Juan Carlos (URJC). He is also the Head of the Bioinspired Systems and Applications Group (SA-BIO). His research interests include stress and emotional states, neurodegenerative diseases, such as Parkinson’s, ALS, and Alzheimer’s, among others, artificial vision, pattern recognition, and biomedical signal processing. He was a recipient of several best paper awards, including ICPRS 2016, BIOSIGNALS 2019, and JID 2020, and the Doctoral Consortium Award from the Spanish Association of Artificial Intelligence, in 2013. He is a reviewer of national and international journal articles.

Cristina Conde, King Juan Carlos University

Cristina Conde Vilda Escuela Tècnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Campus de Mostoles, Madrid, Spain Cristina Conde Vilda received the B.S. degree in physics (electronics) from the Complutense University of Madrid, in 1999, and the Ph.D. degree from Universidad Rey Juan Carlos, Madrid, in 2006. She has worked in the private sector for several years. In 2001, she joined Universidad Rey Juan Carlos, as an Assistant Professor. For seven years, she was the Vice Dean of Studies with the Computer Science School. She is currently a Full Professor. She has coordinated several national and European projects. Her research interests include image and video analysis, pattern recognition, and machine learning in both classical and biologically inspired computation.

Enrique Cabello, King Juan Carlos University

Enrique Cabello Escuela Tècnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Campus de Mostoles, Madrid, Spain Enrique Cabello (Member, IEEE) received the B.S. degree in physics (electronics) from the University of Salamanca and the Ph.D. degree from the Polytechnic University of Madrid. In 1990, he joined the Computer Science Department, University of Salamanca. He joined Universidad Rey Juan Carlos, in 1998, where he has been the Head of the Face Recognition and Artificial Vision Group, since 2001. He is currently a Full Professor. His research interests include image and video analysis, pattern recognition, and machine learning using classic and bioinspired approaches.


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How to Cite

Oulad-Kaddour, M., Haddadou, H., Palacios-Alonso, D., Conde, C., & Cabello, E. (2024). Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 11(2), e3. https://doi.org/10.4108/eetinis.v11i2.4318