Abstract
Gender recognition is one of the challenges employed in computer vision. The current paper has proposed a Real-time system for gender recognition based on a Convolutional neural network. A convolutional neural network is defined as a deep learning algorithm used for image recognition and image classification. A real-time gender recognition system has the capability to recognize either the face of the person, whether is a male or female, even with having complex face images of variations. Many fields need this system to be utilized for security and identification purposes. The gender recognition model is built from scratch and trained on 10182 face images, out of which are 5027 Males and 5155 Females. The model has achieved 97.84% accuracy on validation data and 98.59% accuracy on training data. The advantage of this model can detect a gender in real-time with high performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Debgupta R., Chaudhuri B.B., Tripathy B.K. (2020) A Wide ResNet-Based Approach for Age and Gender Estimation in Face Images. In: Khanna A., Gupta D., Bhattacharyya S., Snasel V., Platos J., Hassanien A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_44
. Wu, Y., Zhuang, Y., Long, X., Lin, F., & Xu, W. (2015). Human gender classification: A review. arXiv preprint arXiv:1507.05122.
. Singh, O., Bommagani, G., Ravula, S. R., & Gunjan, V. K. (2013). Pattern based gender classification. International Journal, 3(10).
. Islam T.U., Awasthi L.K., Garg U. (2021) Gender and Age Estimation from Gait: A Re-view. In: Gupta D., Khanna A., Bhattacharyya S., Hassanien A., Anand S., Jaiswal A. (eds) International Conference on Innovative Computing and Communications. Advances in Intel-ligent Systems and Computing, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-15-5148-2_82
. Tivive, F. H. C., & Bouzerdoum, A. (2006, July). A gender recognition system using shunting inhibitory convolutional neural networks. In The 2006 IEEE International Joint Conference on Neural Network Proceedings (pp. 5336-5341). IEEE.
Liew, S. S., Hani, M. K., Radzi, S. A., & Bakhteri, R. (2016). Gender classification: a convolutional neural network approach. Turkish Journal of Electrical Engineering & Computer Sciences, 24(3), 1248–1264.
. Wu, W., Protopapas, P., Yang, Z., & Michalatos, P. (2020, July). Gender Classification and Bias Mitigation in Facial Images. In 12th ACM Conference on Web Science (pp. 106–114).
. Vani, A. K., Raajan, R. N., Winmalar, D. H., & Sudharsan, R. (2020, March). Using the Keras Model for Accurate and Rapid Gender Identification through Detection of Facial Features. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 572–574). IEEE.
. Kumar, S., Singh, S., & Kumar, J. (2019, January). Gender classification using machine learning with multi-feature method. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0648–0653). IEEE.
. Nair, R. R., Madhavankutty, R., & Nema, S. (2019). Automated Detection of Gender from Face Images.
. Deng, Q., Xu, Y., Wang, J., & Sun, K. (2015, November). Deep learning for gender recognition. In 2015 International Conference on Computers, Communications, and Systems (ICCCS) (pp. 206–209). IEEE.
. Lawal, C. O., Akinrinmade, A. A., & Badejo, J. A. (2019, December). Face-based Gender recognition Analysis for Nigerians Using CNN. In Journal of Physics: Conference Series (Vol. 1378, No. 3, p. 032014). IOP Publishing.
Makinen, E., & Raisamo, R. (2008). Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3), 541–547.
Baluja, S., & Rowley, H. A. (2007). Boosting sex identification performance. International Journal of Computer Vision, 71(1), 111–119.
Mansanet, J., Albiol, A., & Paredes, R. (2016). Local deep neural networks for gender recognition. Pattern Recognition Letters, 70, 80–86.
https://www.kaggle.com/cashutosh/gender-classification-dataset.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Alsellami, B., Deshmukh, P.D. (2022). Gender Recognition Using Deep Leering Convolutional Neural Network. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1387. Springer, Singapore. https://doi.org/10.1007/978-981-16-2594-7_29
Download citation
DOI: https://doi.org/10.1007/978-981-16-2594-7_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2593-0
Online ISBN: 978-981-16-2594-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)