Human gender plays an imperative role as social construct and an essential form of an individual’s personality. Gender recognition is a fundamental task for human beings. It is highly reflected in social communication, forensic science, surveillance, and target marketing. Gender recognition previously depended only on standard face images. Using the term “standard” means that the image was taken in a standard light without any background variations and without any cropped parts. But this type of image is not found in real-world. These images are called non-standard images, as they have a lot of variations, like illumination and head pose. The image may also have a lot of faces, where one of them may wear sunglasses or other accessories. Using this type of image will affect the accuracy results of gender recognition approaches. Nowadays, selfie images appear as they are unconstrained images. People take selfie images of themselves. Selfie images are very complex, as some parts of the images are cropped and damaged. This paper proposes a (CNNGA) technique for gender recognition from selfie images by merging a deep learning approach with genetic algorithms. The proposed technique achieves 90.2% accuracy in recognizing gender from the selfie dataset. The experiments use various challenge datasets, which are widely adopted in the scientific community like LFW, Data Hub, FERET, and Caltech-web Faces.
References
Anwar, S., Hwang, K., Sung, W (2017).: Structured pruning of deep convolutional neural networks. ACM Journal on Emerging Technologies in Computing Systems (JETC) 13(3), 1–18
Arigbabu, O.A., Ahmad, S.M.S., Adnan, W.A.W., Yussof, S., Mahmood, S.: (2017) Soft biometrics: Gender recognition from unconstrained face images using local feature descriptor.
Azzopardi, G., Foggia, P., Greco, A., Saggese, A., Vento, M.: (2018) Gender recognition from face images using trainable shape and color features. In: 2018 24th International conference on pattern recognition (ICPR), pp. 1983–1988. IEEE
Bashar, D.A(2019).: Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence and Capsule Networks 1(2), 73–82
Bhandare, A., Kaur, D.: (2021) Designing convolutional neural network architecture using genetic algorithms. International Journal of Advanced Network, Monitoring and Controls 6(3), 26–35
Buolamwini, J.A.: (2017) Gender shades: intersectional phenotypic and demographic evaluation of face datasets and gender classifiers. Ph.D. thesis, Massachusetts Institute of Technology
Ha, C., Tran, V.D., Van, L.N., Then, K. (2019): Eliminating overfitting of probabilistic topic models on short and noisy text: The role of dropout. International Journal of Approximate Reasoning 112, 85–104
Hamouda, M., Ettabaa, K.S., Bouhlel, M.S. (2020): Smart feature extraction and classification of hyperspectral images based on convolutional neural networks. IET Image Processing 14(10), 1999–2005
Hsu, C.Y., Lin, L.E., Lin, C.H. (2021): Age and gender recognition with random occluded data augmentation on facial images. Multimedia Tools and Applications 80, 11,631–11,653
Kammerer, L., Kronberger, G., Burlacu, B., Winkler, S.M., Kommenda, M., Affenzeller, M.: (2020) Symbolic regression by exhaustive search: Reducing the search space using syntactical constraints and efficient semantic structure deduplication. Genetic programming theory and practice XVII pp. 79–99
Li, B., Lian, X.C., Lu, B.L.: (2012) Gender classification by combining clothing, hair, and facial component classifiers. Neurocomputing 76(1), 18–27
Liew, S.S., Hani, M.K., Radzi, S.A., Bakhteri, R. (2016): Gender classification: a convolutional neural network approach. Turkish Journal of Electrical Engineering and Computer Sciences 24(3), 1248– 1264
Mehboob, U., Qadir, J., Ali, S., Vasilakos, A (2016).: Genetic algorithms in wireless networking: techniques, applications, and issues. Soft Computing 20, 2467–2501
Mishra, P., Singh, N., Chavan, P (2021).: Real time gender classification using convolutional neural network. In: ITMWeb of Conferences, vol. 40, p. 03047. EDP Sciences
Montana, D.J., Davis, L., et al. (1989): Training feedforward neural networks using genetic algorithms. In: IJCAI, vol. 89, pp. 762–767
Ren, S., He, K., Girshick, R., Zhang, X., Sun, J. (2016): Object detection networks on convolutional feature maps. IEEE transactions on pattern analysis and machine intelligence 39(7), 1476–1481
Ruan, C.: (2017) Genetic algorithms. Tersedia di: www. professorbray. net/¿[Diakses pada 30 Agustus 2018]
Santarcangelo, V., Farinella, G.M., Battiato, S. (2015): Gender recognition: methods, datasets, and results. In: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1– 6. IEEE
Schaffer, J.D., Whitley, D., Eshelman, L.J.: (1992) Combinations of genetic algorithms and neural networks: A survey of the state of the art. In: [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks, pp. 1–37. IEEE
Shah, P. (2020): Design space exploration of convolutional neural networks for image classification. Ph.D. thesis
Shekar, G., Revathy, S., Goud, E.K.: (2020) Malaria detection using deep learning. In: 2020 4th international conference on trends in electronics and informatics (ICOEI) (48184), pp. 746–750. IEEE
Srinivas, M., Patnaik, L.M. (1994): Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics 24(4), 656–667
Vikhar, P.A. (2016): Evolutionary algorithms: A critical review and its prospects. In: 2016 International conference on global trends in signal processing, information computing and communication (ICGTSPICC), pp. 261–265. IEEE
Xin, M., Wang, Y. (2019): Research on image classification model based on deep convolution neural network. EURASIP Journal on Image and Video Processing 2019, 1–11
Yuan, X., Feng, Z., Norton, M., Li, X. (2019): Generalized batch normalization: Towards accelerating deep neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 1682–1689
