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Automatic Students Attendance Marking System Using Image Processing And Machine Learning

机译:利用图像处理和机器学习的自动学生出勤阅卷系统

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To track student attendance, colleges and schools use traditional methods involving manual marking on sheets. These methods are timeconsuming and tedious for teachers. The proposed system automatically records the student’s attendance during lecture hours using facial recognition technology with image processing instead of the traditional manual methods. The proposed system works in a controlled environment, in which students, face images are captured and then upon recognition, their attendance is automatically marked in an Excel sheet. Student’s face is detected using the Viola-Jones technique, whereas Linear Discriminate Analysis (LDA) along with KNN and SVM is used for face recognition. Experimentation shows that it provides better accuracy than the existing PCA and other techniques.
机译:为了跟踪学生的出勤率,大学和学校使用传统的方法,包括在纸张上手动标记。这些方法对于教师而言既费时又乏味。拟议的系统使用具有图像处理功能的面部识别技术代替传统的手动方法,自动在授课时间记录学生的出勤情况。拟议的系统在受控环境中工作,在该环境中,将捕获学生,面部图像,然后在识别后将其出勤率自动标记在Excel表中。使用Viola-Jones技术检测学生的面部,而线性判别分析(LDA)以及KNN和SVM则用于面部识别。实验表明,与现有的PCA和其他技术相比,它提供了更好的准确性。

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