A Novel Approach for Facial Attendance:AttendXNet
Keywords:Feature extraction, Support Vector Machine, Muli-layer Neural Network, Face Anti-spoofing, Faiss
Recent advancements in the area of facial recognition and verification introduced the possibility of facial attendance for various use cases. In this paper we present a system named as AttendXNet. Our method uses the ResNet and Multi-layer feed forward network to achieve the state of art results. Extensive analysis of various deep learning and machine learning techniques is described. Face anti-spoofing is a major challenge in facial attendance. Extended-MobileNet is used to resolve the same issue. We also introduced the end to end pipeline to implement an attendance system for various use cases.
 Gary B. Huang , Manu Ramesh, Tamara Berg , and Erik Learned-Miller . Labeled Faces in
the Wild: A Database for Studying Face Recognition in Unconstrained Environments.
University of Massachusetts, Amherst, Technical Report 07-49 , October, 2007.
 Taigman, Yaniv, et al. "Deepface: Closing the gap to human-level performance in face
verification." Proceedings of the IEEE conference on computer vision and pattern
recognition . 2014.
 W.-S. T. WST. Deeply learned face representations are sparse, selective, and robust.
perception, 31:411–438, 2008.
 Sun, Yi, et al. "Deepid3: Face recognition with very deep neural networks." arXiv preprint
 Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A unified embedding
for face recognition and clustering." Proceedings of the IEEE conference on computer
vision and pattern recognition . 2015.
 J. Liu, Y. Deng, T. Bai, Z. Wei, and C. Huang. Targeting ultimate accuracy: Face
recognition via deep embedding. arXiv preprint arXiv:1506.07310, 2015.
 O. M. Parkhi, A. Vedaldi, A. Zisserman, et al. Deep face recognition. In BMVC, volume 1,
page 6, 2015.
How to Cite
Copyright (c) 2020 Kawal Arora, Ankur Singh Bist, Roshan Prakash, Saksham Chaurasia
This work is licensed under a Creative Commons Attribution 4.0 International License.
This journal permits and encourages authors to post items submitted to the journal on personal websites while providing bibliographic details that credit its publication in this journal.
Authors are permitted to post their work online in institutional/disciplinary repositories or on their own websites. Pre-print versions posted online should include a citation and link to the final published version in Journal of Librarianship and Scholarly Communication as soon as the issue is available; post-print versions (including the final publisher's PDF) should include a citation and link to the journal's website.