A Novel Approach for Facial Attendance:AttendXNet

Authors

  • Kawal Arora Signy Advanced Technologies
  • Ankur Singh Bist Signy Advanced Technologies
  • Roshan Prakash Signy Advanced Technologies
  • Saksham Chaurasia Signy Advanced Technologies

DOI:

https://doi.org/10.34306/att.v2i2.86

Keywords:

Feature extraction, Support Vector Machine, Muli-layer Neural Network, Face Anti-spoofing, Faiss

Abstract

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.

References

References
[1] 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.
[2] 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.
[3] W.-S. T. WST. Deeply learned face representations are sparse, selective, and robust.
perception, 31:411–438, 2008.
[4] Sun, Yi, et al. "Deepid3: Face recognition with very deep neural networks." arXiv preprint
arXiv:1502.00873 (2015).
[5] 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.
[6] 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.
[7] O. M. Parkhi, A. Vedaldi, A. Zisserman, et al. Deep face recognition. In BMVC, volume 1,
page 6, 2015.

Additional Files

Published

2020-06-05

How to Cite

Arora, K., Bist, A. S., Prakash, R., & Chaurasia, S. (2020). A Novel Approach for Facial Attendance:AttendXNet. Aptisi Transactions on Technopreneurship (ATT), 2(2), 104 - 111. https://doi.org/10.34306/att.v2i2.86