Real-world human gender classification from oral region using convolutional neural netwrok

  • Mohamed Oulad-Kaddour
    Ecole naionale Supéerieure en Informatique m_ouled_kaddour[at]esi.dz
  • Hamid Haddadou
    Ecole naionale Supéerieure en Informatique
  • Cristina Conde
    Rey Juan Carlos University
  • Daniel Palacios-Alonso
    Rey Juan Carlos University
  • Enrique Cabello
    Rey Juan Carlos University

Abstract

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.
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Affifi, M., 2019. 11K hands: Gender recognition and biometric identification using a large dataset of hand images. In Multimedia Tools and Applications.

Afifi, M., and Abdelhamed, A., 2019. AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces. In Journal of Visual Communication and Image Representation. Elsivier.

Alzubaidi, L., Zhang, J., Humaidi, A., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M., Al-Amidie, M., and Laith, F., 2021. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. In J Big Data. Springer.

Carrie, W., and Darryl, W. S., 2020. Understanding visual lip-based biometric authentication for mobile devices. In EURASIP J. on Info. Security.

Choraś, M., 2010. The lip as a biometric. Pattern Anal Applic 13, 105–112. https://doi.org/10.1007/s10044-008-0144-8

Darryl, S., Adrian, P., and Jianguo, Z., 2013. Gender classification via lips: static and dynamic features. In IET Biometrics.

Jiankang, D., Jia, G., Yuxiang, Z., Jinke, Y., Irene, K., and Stefanos, Z., 2019. RetinaFace: Single-stage Dense Face Localisation in the Wild. https://doi.org/10.48550/arXiv.1905.06641

Jie, C., Haiqing, L., Zhenan, S., and Ran, H., 2016. Accurate mouth state estimation via convolutional neural networks. In IEEE International Conference on Digital Signal Processing (DSP). IEEE.

Karras, T., Laine, S., and Aila, T., 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4401–4410. IEEE/CVF.

Li B., Lian X. C., and Lu B. L., 2012. Gender classification by combining clothing, hair and facial component classifiers. In Neurocomputing. Elsivier.

Rai, P., and Khanna, P., 2014. A gender classification system robust to occlusion using Gabor features based (2D)2PCA. In J. Vis. Commun. Image R.

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D., 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 618–626. IEEE.

Shrestha, K., 2018. Lip Reading using Neural Network and Deep learning. https://doi.org/10.48550/arXiv.1802.05521.

Tapia, J., and Carlos, A., 2017. Gender Classification from NIR Iris Images Using Deep Learning. In Deep Learning for Biometrics. Springer.

Tarare, S., Anjikar, A., and Turkar, H., 2015. Fingerprint Based Gender Classification Using DWT Transform. In International Conference on Computing Communication Control and Automation.

Viedma, I., Tapia, J., Iturriaga, A., and Busch, C., 2019. Relevant features for gender classification in NIR periocular images. In IET Biom., page 340–350.

Wu, T. X., Lian, X. C., and Lu, B. L., 2012. Multi-view gender classification using symmetry of facial images. In Neural Comput. Appl.

Yaman, D., Eyiokur, F. I., Sezgin, N., and Ekenel, H. K., 2018. Age and gender classification from ear images. In In International Workshop on Biometrics and Forensics. IEEE.

Yannis, M. A., Brendan, S., Shimon, W., and Nando, d. F., 2016. LipNet: End-to-End Sentence-level Lipreading. https://doi.org/10.48550/arXiv.1611.01599.

Oulad-Kaddour, M., Haddadou, H., Conde, C., Palacios-Alonso, D., & Cabello, E. (2023). Real-world human gender classification from oral region using convolutional neural netwrok . ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(3), 249–261. https://doi.org/10.14201/adcaij.27797

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