The Growing Interest of computer vision within the past decade. From a preferred computer vision is an area of research , it has been found that a complex problem of computer vision is rise above by face detection and recognition and it provide us one of the better and successful analysis the image of applications and unable to understand the algorithm. Because of the essential nature of the problem, In area of research computer science and computer vision make use of neuro-scientific and psychological studies. It mainly use for the purpose of processing the computer image that advances in nature and help us to understand the research that provide awareness that how our brain work and vice versa. Many developer have various purpose of making different applications for the users that can be access on different platforms which mainly work to analysis carefully the facial features of the person. This application based on computer vision which is an open source named as Intel’s , OpenCV and framework.

face detection, face recognition, opencv, NumPy

[1] B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” Proceedings of the DARPA imaging understanding workshop, pp. 121–130, 1981.
[2] Lucas-Kanade 20 years on: A unifying framework” Baker and Mathews, IJCV 04.
[3] Lyons, Michael J., Shigeru Akamatsu, Miyuki Kamachi, Jiro Gyoba, and Julien Budynek. ”The Japanese female facial expression (JAFFE) database.” third international conference on automatic face and gesture recognition, pp. 14-16, 1998.
[4] Ian Goodfellow et al. Challenges in Representation Learning: A report on three machine learning contests, 2013
[5] Y. Tian, T. Kanade and J. Cohn, “Recognizing Action Units for Facial Expression Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 97–115, 2001.
[6] Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. “Spatial transformer networks.” Advances in neural information processing systems, 2015.
[7] Shhervin Minaee, Amirali Abdolrashidi, “Deep emotion:Facial Expression Recognition Using Attentional Convolutional Network”.
[8] J.Y. Bouguet, “Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm,” Intel Corporation, Microprocessor Research Labs, pp. 1-9, 2000.
[9] Lucey, Patrick, et al. “The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression.” Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference on. IEEE, 2010.
[10] Vinay Bettadapura,“Face Expression Recognition and Analysis:The State of the Art”.
[11] Octavio Arriaga, Paul G. Ploger, Matias Valdenegro, “Real Time convolutional Neural Networks for Emotion and Gender Classification”.
[12] Shan, Caifeng, Shaogang Gong, and Peter W. McOwan. “Facial expression recognition based on local binary patterns: A comprehensive study.” Image and vision Computing 27.6: 803-816, 2009.
[13] Dario Amodei et al. Deep speech 2: End-to-end speech recognition in english and mandarin. CoRR, abs/1512.02595, 2015.
[14] Official site of Opencv.
[15] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pages 315–323, 2011.
[16] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2818–2826, 2016.
[17] N.Sudhakar Reddy, M.V.Sumanth, S.Suresh Babu, "A Counterpart Approach to Attendance and Feedback System using Machine Learning Techniques",Journal of Emerging Technologies and Innovative Research (JETIR), Volume 5, Issue 12, Dec 2018.
[18] Dan Wang, Rong Fu, Zuying Luo, "Classroom Attendance Auto-management Based on Deep Learning",Advances in Social Science, Education and Humanities Research, volume 123,ICESAME 2017.
[19] Akshara Jadhav, Akshay Jadhav, Tushar Ladhe, Krishna Yeolekar, "Automated Attendance System Using Face Recognition", International Research Journal of Engineering and Technology (IRJET), Volume 4, Issue 1, Jan 2017.
[20] B Prabhavathi, V Tanuja, V Madhu Viswanatham and M Rajashekhara Babu, "A smart technique for attendance system to recognize faces through parallelism", IOP Materials Science and Engineering 263, 201