Abstract

Human gender plays an imperative role as a social construct and an essential form of an individual’s personality. This is highly reflected in social communication, forensic science, surveillance, and targeted marketing. The task has always been practiced in conjunction with face recognition, as it yields high performance compared to other features, e.g., gait and sound. This is attributed to its richness with diverse useful clues for gender inspection. However, facial features were always used separately without any integration between them, and a definitive solution has not yet been found yet. We propose a robust feature fusion method that integrates various facial features, in addition to a set of new features. Furthermore, Haar-like features are used to extract the essential features, while the new features used are, i.e., beard, moustache, cheeks, forehead, and face. The scientific novelty of the research is the robust fusion of features to achieve high recognition accuracy. The proposed fusion method is a priority-based method that arranges classifiers’ responses from the strongest to weakest according to their importance to yield a final decision. When the proposed technique compares the recent and standard benchmarks, it achieves high recognition accuracy. The experiments on various standard and challenging datasets widely adopted in the scientific community, namely LFW, DataHub, NIST, and Caltech WebFaces, demonstrated a robust performance of 99.1% accuracy.

References

  1. 1. Bekhet, S., Alghamdi, A. M., & Taj-Eddin, I. (2022). Gender recognition from unconstrained selfie images: a convolutional neural network approach. International Journal of Electrical and Computer Engineering (IJECE), 12(2), 2066-2078.
  2. 2. Bekhet, S., & Alahmer, H. (2021). A robust deep learning approach for glasses detection in non‐standard facial images. IET Biometrics, 10(1), 74-86.
  3. 3. Choudhury, S., Chattopadhyay, S. P., & Hazra, T. K. (2017, August). Vehicle detection and counting using haar feature-based classifier. In 2017 8th annual industrial automation and electromechanical engineering conference (IEMECON) (pp. 106-109). IEEE.
  4. 4. Egorov, A. D. (2018). Algorithm for optimization of Viola–Jones object detection framework parameters. In Journal of Physics: Conference Series (Vol. 945, No. 1, p. 012032). IOP Publishing.
  5. 5. Bhardwaj, A., Karger, D., Subramanyam, H., Deshpande, A., Madden, S., Wu, E., ... & Zhang, R. (2015, August). Collaborative data analytics with DataHub. In Proceedings of the VLDB Endowment International Conference on Very Large Data Bases (Vol. 8, No. 12, p. 1916).
  6. 6. Upchurch, P., Gardner, J., Pleiss, G., Pless, R., Snavely, N., Bala, K., & Weinberger, K. (2017). Deep feature interpolation for image content changes. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7064-7073).
  7. 7. Hermosilla, G., Ruiz-del-Solar, J., & Verschae, R. (2017). An enhanced representation of thermal faces for improving local appearance-based face recognition. Intelligent Automation & Soft Computing, 23(1), 1-12.
  8. 8. Dou, J., Qin, Q., & Tu, Z. (2018). Robust image matching based on the information of SIFT. Optik, 171, 850-861.
  9. 9. Sharma, D., Zafar, S., Babaie, M., & Tizhoosh, H. R. (2018, November). Facial recognition with encoded local projections. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1261-1268). IEEE.
  10. 10. Calder, A. J., & Young, A. W. (2016). Understanding the recognition of facial identity and facial expression. Facial Expression Recognition, 41-64.
  11. 11. Sensors, J. O. (2023). Retracted: Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor.
  12. 12. Nguyen, D. T., & Park, K. R. (2016). Enhanced gender recognition system using an improved histogram of oriented gradient (HOG) feature from quality assessment of visible light and thermal images of the human body. Sensors, 16(7), 1134.
  13. 13. Bruno, D. R., Berri, R. A., Barbosa, F. M., & Osório, F. S. (2023). CARINA Project: Visual perception systems applied for autonomous vehicles and advanced driver assistance systems (ADAS). IEEE Access, 11, 69720-69749.
  14. 14. Hossam, M., Afify, A. A., Rady, M., Nabil, M., Moussa, K., Yousri, R., & Darweesh, M. S. (2021, July). A comparative study of different face shape classification techniques. In 2021 International Conference on Electronic Engineering (ICEEM) (pp. 1-6). IEEE.
  15. 15. Dadi, H. S., & Pillutla, G. M. (2016). Improved face recognition rate using HOG features and SVM classifier. IOSR Journal of Electronics and Communication Engineering, 11(04), 34-44.
  16. 16. Richmond, S., Howe, L. J., Lewis, S., Stergiakouli, E., & Zhurov, A. (2018). Facial genetics: a brief overview. Frontiers in genetics, 9, 462.
  17. 17. Khan, K., Attique, M., Syed, I., & Gul, A. (2019). Automatic gender classification through face segmentation. Symmetry, 11(6), 770.
  18. 18. Fekri-Ershad, S. (2020). Developing a gender classification approach in human face images using modified local binary patterns and tani-moto based nearest neighbor algorithm. arXiv preprint arXiv:2001.10966.
  19. 19. Anwarul, S., & Dahiya, S. (2019). A comprehensive review on face recognition methods and factors affecting facial recognition accuracy. Proceedings of ICRIC 2019: Recent Innovations in Computing, 495-514.
  20. 20. Azzopardi, G., Greco, A., & Vento, M. (2016, August). Gender recognition from face images with trainable COSFIRE filters. In 2016 13th IEEE international conference on advanced video and signal based surveillance (AVSS) (pp. 235-241). IEEE.
  21. 21. Althnian, A., Aloboud, N., Alkharashi, N., Alduwaish, F., Alrshoud, M., & Kurdi, H. (2020). Face gender recognition in the wild: an extensive performance comparison of deep-learned, hand-crafted, and fused features with deep and traditional models. Applied Sciences, 11(1), 89.
  22. 22. Ng, C. B., Tay, Y. H., & Goi, B. M. (2015). A review of facial gender recognition. Pattern Analysis and Applications, 18, 739-755.
  23. 23. Li, Z., Jiao, Z., & He, A. (2020). Knowledge‐based artificial neural network for power transformer protection. IET Generation, Transmission & Distribution, 14(24), 5782-5791.
  24. 24. Raji, I. D., & Fried, G. (2021). About face: A survey of facial recognition evaluation. arXiv preprint arXiv:2102.00813.
  25. 25. Tolba, M., & Moustafa, M. (2016). GAdaBoost: accelerating adaboost feature selection with genetic algorithms. arXiv preprint arXiv:1609.06260.
  26. 26. Kaddar, B., Fizazi, H., & Boudraa, A. O. (2018). Texture features based on an efficient local binary pattern descriptor. Computers & Electrical Engineering, 70, 496-508.
  27. 27. Akhmet, M., Tleubergenova, M., & Zhamanshin, A. (2022). Dynamics of shunting inhibitory cellular neural networks with variable two-component passive decay rates and Poisson stable inputs. Symmetry, 14(6), 1162.
  28. 28. Adnan, S., Ali, F., & Abdulmunem, A. A. (2020, November). Facial feature extraction for face recognition. In Journal of Physics: Conference Series (Vol. 1664, No. 1, p. 012050). IOP Publishing.
  29. 29. Rustam, Z., & Ruvita, A. A. (2018, November). Application support vector machine on face recognition for gender classification. In Journal of Physics: Conference Series (Vol. 1108, No. 1, p. 012067). IOP Publishing.
  30. 30. Kumar, S., Singh, S., & Kumar, J. (2017, May). A study on face recognition techniques with age and gender classification. In 2017 International Conference on Computing, Communication and Automation (ICCCA) (pp. 1001-1006). IEEE.