Comparison between attendance system implemented through haar cascade classifier and face recognition library

Face detection and face recognition are the most widely used features of machine learning(ML) and deep learning(DL). These features are slowly gaining popularity in fields like surveillance through CCTV cameras, mobile phone security (biometric locks), etc. This paper presents a college attendance system based on the above mentioned feature which automatically marks the attendance of the students through the live feed by the CCTV cameras in the classroom. This system saves time, works more efficiently, then manually marking the attendance by the roll call. System was built with the haar cascade features, open CV and face recognition library. These are discussed in the proposed paper. We have presented a comparison between the two models discussed and concluded that haar cascade features work more efficiently than the inbuilt face recognition library for the college attendance system.

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