Smart home Management System with Face Recognition Based on ArcFace Model in Deep Convolutional Neural Network

T. Dang
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引用次数: 6

Abstract

In recent years, artificial intelligence has proved its potential in many fields, especially in computer vision. Facial recognition is one of the most essential tasks in the field of computer vision with various prospective applications from academic research to intelligence service. In this paper, we propose an efficient deep learning approach to facial recognition. Our approach utilizes the architecture of ArcFace model based on the backbone MobileNet V2, in deep convolutional neural network (DCNN). Assistive techniques to increase highly distinguishing features in facial recognition. With the supports of the facial authentication combines with hand gestures recognition, users will be able to monitor and control his home through his mobile phone/tablet/PC. Moreover, they communicate with data and connect to smart devices easily through IoT technology. The overall proposed model is 97% of accuracy and a processing speed of 25 FPS. The interface of the smart home demonstrates the successful functions of real-time operations.
基于ArcFace模型的深度卷积神经网络人脸识别智能家居管理系统
近年来,人工智能已经在许多领域证明了它的潜力,特别是在计算机视觉方面。人脸识别是计算机视觉领域最重要的任务之一,从学术研究到情报服务都有广泛的应用前景。在本文中,我们提出了一种高效的深度学习人脸识别方法。我们的方法在深度卷积神经网络(DCNN)中利用基于骨干MobileNet V2的ArcFace模型架构。在面部识别中增加高度显著特征的辅助技术。在面部认证与手势识别相结合的支持下,用户可以通过手机/平板电脑/PC对自己的家进行监控。此外,它们还可以通过物联网技术轻松地与数据通信并连接到智能设备。该模型的总体精度为97%,处理速度为每秒25帧。智能家居界面展示了实时操作的成功功能。
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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CiteScore
6.30
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