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Real-time Attendance using Machine Learning and Face Detection Technique

Prashik Tantarpale, Rishikesh Puri, Ravindra Pawar, Bhagyashri Sonawale

Abstract


If attendance is managed manually, it may be quite stressful on the instructors. Smart and automated attendance management systems are being used to address this issue. But with this system, authentication is a crucial problem. The smart attendance system often uses biometrics to operate. Face recognition is one of the biometric approaches used to improve and enhance this system. Recognizing individuals by their faces in photos and video streams is commonplace, from social media to phone cameras. A face recognition system is designed to match with human faces using computer images. Your brain is built to accomplish all of this naturally and instantaneously as a human. In reality, humans are overly adept at detecting faces, and as a result, they perceive faces in common items. This kind of generality is not possible for computers. Face recognition has been used in computer vision for a long time and has advanced significantly over time. This approach has a variety of uses, including user identification and verification. This Face attendance project uses face recognition for recording attendance accurately and more efficiently using HOG and neural network that puts 128 unique measurements to a particular face, to compare the package uses one of the most common machine learning methods linear SVM classifier, and registers attendance.

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References


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DOI: https://doi.org/10.37591/ijowns.v8i1.816

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