Comparative analysis of facial recognition models using video for real time attendance monitoring system

Payal Patil, S. Shinde
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引用次数: 7

Abstract

Attendance reporting is one of the standard processes across the world in academic institutions. The key purpose is to encourage consistency in attending school which in turn improves the learning process for a student. The manual attendance system is widely used in the educational system which is time-consuming as well as laborious. The main concept behind the automatic attendance system is to apply facial recognition effortlessly compared to other biometric systems. Following three methods i.e. Histogram Oriented Gradients (HOG), Viola-Jones (Haar Cascade), and Convolution Neural Network (CNN) are analyzed based on face detection accuracy. The Viola-Jones method delivered high accuracy amongst all. For real-time attendance systems, Viola-Jones and CNN algorithms are utilized for face detection and recognition purposes respectively. A benefit of the recommended system is to overcome hurdles like moderately detectable faces, objectionable light conditions, and alignments. The proposed system achieved 94.6% accuracy on a real-time database.
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人脸识别模型应用于视频实时考勤监控系统的对比分析
出勤报告是世界各地学术机构的标准流程之一。其主要目的是鼓励学生坚持上学,从而改善学生的学习过程。人工考勤系统在教育系统中被广泛使用,既费时又费力。与其他生物识别系统相比,自动考勤系统背后的主要概念是毫不费力地应用面部识别。基于人脸检测精度分析了直方图定向梯度(HOG)、Viola-Jones (Haar Cascade)和卷积神经网络(CNN)三种方法。在所有方法中,维奥拉-琼斯方法提供了较高的准确性。对于实时考勤系统,分别使用Viola-Jones和CNN算法进行人脸检测和识别。推荐的系统的一个好处是克服了一些障碍,如中度可检测的面孔,令人反感的光线条件和对齐。该系统在实时数据库上的准确率达到94.6%。
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