Sai Kiran Chintalapudi, Prajwal Reddy, Keerthi Shrikar Tatapudi


Professors who teach at Universities spend a lot of time for taking attendances in different classes(slots). If the class strength exceeds 60, then it will be a problem for them as they should take the attendance and teach also in the same hour. If  they should mark present to a student later, then they must search for his name or any of his unique ID. Also, students will try to fake the attendance as they have to just say yes for a number which will increase the amount of proxies in a class. As it doesn’t matter for teachers, they somehow are satisfied until any one is caught red-handed. Here, we have put to use the machine learning algorithms to register the attendance using face recognition.



Attendance, recognition, product, histogram, functions.

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Automatic Attendance Management System Using Face Recognition Jomon Joseph1, K. P.Zacharia2.

M. Hu and B. Liu, "Mining and summarizing customer reviews," Proceedings of the tenth ACM international conference on Knowledge discovery and data mining, Seattle,2004, pp. 168-177.

B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques,” Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol.10, 2002, pp. 79-86.

K. Dave, S. Lawrence, and D. M. Pennock,“Mining the peanut gallery: Opinion extraction and semantic classification of product reviews,” Proceedings of WWW, 2003, pp. 519–528.

R. Prabowo and M. Thelwall, "Sentiment analysis: A combined approach" , Journal of Informetrics, vol. 3, pp.143-157, 2009.

Ankitha Srivastava, Dr.M.P. Singh, “Supervised SA of product reviews usin Weighted k-NN Algorithm,” 2014 11th International Conference on Information Technology.

Ji Fang and Bi Chen, “Incorporating Lexicon Knowledge into SVM Learning to Improve Sentiment Classication”, In Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP), pages 94–100, 2011.

A. Mudinas, D. Zhang, M. Levene, “Combining lexicon and learning based approaches for conceptlevel sentiment analysis”, Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, ACM, New York,NY, USA, Article 5, pp. 1-8, 2012.



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