Face Recognition is a dynamic research territory in the example acknowledgment and PC vision areas. This has numerous fundamental applications, for example, examination, Visa, travel permit, wellbeing, and so on. Various techniques have been proposed in the most recent decades. Because of the idea of the issue, researchers, and analysts all offer a distinct fascination in this field. CNN system for confront acknowledgment was the best in class profound learning approaches for confront acknowledgment assignments. Because of the way that CNNs accomplish the best outcomes for bigger datasets, which isn't the situation underway condition, the principle challenge was applying these strategies on littler datasets. Another approach which utilizes DWT and QSWT for picture expansion for confront acknowledgment errands is proposed and is reasonable for delivering better outcomes in littler datasets. The proposed confront acknowledgment model could be incorporated in another framework with or without some minor shifts as a supporting or a primary segment for checking purposes.
Face detection, SVM classifier, CNNs, Image Augmentation and CCTV camera
Spitsyn V.G. Tomsk , Polytechnic University , Tomsk, Russia. “Compact Convolutional Neural Network Cascade for Face Detection”.
Klemen Grm, Vitomir ,Struc, Anais Artiges, Matthieu Caron, Hazim Kemal Ekenel “ Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations”.
Huaizu Jiang, University of Massachusetts , Amherst ,Amherst MA 01003 “Face Detection with the Faster R-CNN”.
Arthishri, Fazeela, Khavya, Monisha, Swarna Varsha, Ece Department, Avinashilingam University, Varapalayam, Coimbatore. “Fingerprint Based Student Attendance System Using Wireless Technology”.
R.Jagadish, R.Divya, C.Rengalakshmi, K.Vidhysree, T.Ponmeena, Department of ECE, Sasurie Academy of Engineering, Coimbatore, India. “Real Time Face Recognition System for Time and Attendance Application”
K. Senthamil Selvi, P.Chitrakala, A. AntonyJenitha. Hindustan University, chennai. “Face recognition Based Attendance Marking System”. Klemen Grm, Vitomir Struc, Anais Artiges , Matthieu Caron, Hazim Kemal Ekenel,” Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations”.
Sugandh Pandey, Krishna Singh, Manish Wadkar ,Hrishikesh Vadalkar, Department of Electronics & Telecommunication Engineering. “Smart Application For Attendance Marking System Using Facial Recognition”.
Muthu Kalyani. K, Veera Muthu.A , M-Tech Information Technology, Sathyabama University, Chennai. “Smart Application For ATMS Using Facial Recognition”.
Nirmalya Kar, Mrinal Kanti Debbarma, Ashim Saha, and Dwijen Rudra Pal, ” Study of Implementing Automated Attendance System Using Face Recognition Technique”.
Navesh Sallawar, Shubhamyende, Vaibhav padgilwar, Vishal kale ,paraggorlewar, Gaurav Varma , B.E. Dept. Of Electronics and telecommunication, JCOET Yavatmal, Maharashtra, India. “ Automatic attendance system by using face recognition”.
Akshara Jadhav, Akshay Jadhav, Tushar Ladhe, Krishna Yeolekar, Dept. of Information Technology, University of Pune, NDMVP’s KBT College Of Engineering, Nashik .“Automated attendance system by using face recognition”.
A. Barbu, N. Lay, and G. Gramajo. Face Detection with a 3D Model. arXiv preprint arXiv:1404.3596, 2014.