A Real-Time Single-Shot Multi-Face Detection, Landmark Localization, and Gender Classification

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI:10.1145/3469951.3469952

T. Shen, D. Wang, Kayton Wai Keung Cheung, M. C. Chan, King Hung Chiu, Yiu Kei Li

引用次数: 1

Abstract

Face detection and gender classification by Deep Neural Networks can find application in areas such as video surveillance, customized advertisement, and human-computer interaction. This paper presents a real-time single-shot multi-face gender detector based on Convolutional neural network (CNN). The proposed method not only detects face but also classifies the gender of persons in the wild, meaning in images with a high variability in pose, illumination, and occlusion. To train and evaluate the results, a new annotated set of face images is created. Our experimental results show that the proposed method achieves excellent performance in term of speed and accuracy.

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实时单镜头多人脸检测、地标定位和性别分类

基于深度神经网络的人脸检测和性别分类可以在视频监控、定制广告、人机交互等领域得到应用。本文提出了一种基于卷积神经网络(CNN)的实时单镜头多人脸性别检测器。所提出的方法不仅可以检测人脸，还可以在野外对人的性别进行分类，这意味着在姿势，照明和遮挡的高度变化的图像中。为了训练和评估结果，创建了一组新的带注释的人脸图像。实验结果表明，该方法在速度和精度方面都取得了优异的成绩。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision

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