Gender Classification Using Proposed CNN-Based Model and Ant Colony Optimization

Farhat Abbas, Mussarat Yasmin, Muhammad Fayyaz, Mohamed Abd Elaziz, Songfeng Lu and Ahmed A. Abd El-Latif
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Farhat Abbas: Department of Computer Science, COMSATS University Islamabad, Wah Campus, WahCantt 47040, Pakistan
Mussarat Yasmin: Department of Computer Science, COMSATS University Islamabad, Wah Campus, WahCantt 47040, Pakistan
Muhammad Fayyaz: Department of Computer Science, COMSATS University Islamabad, Wah Campus, WahCantt 47040, Pakistan
Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Songfeng Lu: Hubei Engineering Research Center on Big Data Security, School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Ahmed A. Abd El-Latif: Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt

Mathematics, 2021, vol. 9, issue 19, 1-29

Abstract: Pedestrian gender classification is one of the key assignments of pedestrian study, and it finds practical applications in content-based image retrieval, population statistics, human–computer interaction, health care, multimedia retrieval systems, demographic collection, and visual surveillance. In this research work, gender classification was carried out using a deep learning approach. A new 64-layer architecture named 4-BSMAB derived from deep AlexNet is proposed. The proposed model was trained on CIFAR-100 dataset utilizing SoftMax classifier. Then, features were obtained from applied datasets with this pre-trained model. The obtained feature set was optimized with ant colony system (ACS) optimization technique. Various classifiers of SVM and KNN were used to perform gender classification utilizing the optimized feature set. Comprehensive experimentation was performed on gender classification datasets, and proposed model produced better results than the existing methods. The suggested model attained highest accuracy, i.e., 85.4%, and 92% AUC on MIT dataset, and best classification results, i.e., 93% accuracy and 96% AUC, on PKU-Reid dataset. The outcomes of extensive experiments carried out on existing standard pedestrian datasets demonstrate that the proposed framework outperformed existing pedestrian gender classification methods, and acceptable results prove the proposed model as a robust model.

Keywords: support vector machine; gender classification; visual surveillance; 4-BSMAB; ACS; CNN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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