A Deep Learning-based Model for Gender Recognition in Mobile Devices

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Alinezhad Fatemeh; Kiani Kourosh; Rastgoo Razieh

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Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2023 | Volume:11 | Issue:2 Page(s): 229-236

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Information Journal Paper

Title

A Deep Learning-based Model for Gender Recognition in Mobile Devices

Author(s)

Alinezhad Fatemeh | Kiani Kourosh | Rastgoo Razieh | Issue Writer Certificate

Keywords

deep learning

Gender recognition

Margin distillation

Dense block

MobileFaceNet

Abstract

Gender recognition is an attractive research area in recent years. To make a user-friendly application for Gender recognition, having an accurate, fast, and lightweight model applicable in a mobile device is necessary. Although successful results have been obtained using the Convolutional Neural Network (CNN), this model needs high computational resources that are not appropriate for mobile and embedded applications. To overcome this challenge and considering the recent advances in deep learning, in this paper, we propose a deep learning-based model for Gender recognition in mobile devices using the lightweight CNN models. In this way, a pretrained CNN model, entitled Multi-Task Convolutional Neural Network (MTCNN), is used for face detection. Furthermore, the MobileFaceNet model is modified and trained using the Margin distillation cost function. To boost the model performance, the Dense block and Depthwise separable convolutions are used in the model. Results on six datasets confirm that the proposed model outperforms the MobileFaceNet model on six datasets with the relative accuracy improvements of 0.02%, 1.39%, 2.18%, 1.34%, 7.51%, 7.93% on the LFW, CPLFW, CFP-FP, VGG2-FP, UTKFace, and own data, respectively. In addition, we collected a dataset, including a total of 100’000 face images from both male and female in different age categories. Images of the women are with and without headgear.

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