Bank Locker Face Recognition.ppt
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This document discusses improving security for bank lockers. It outlines problems with current alarm and CCTV technologies that do not fully secure lockers. It proposes using a hidden image scanner and face detection algorithm to address this. The face detection algorithm uses color information to find skin regions, thresholds the image, and verifies faces using a template. Implementing this image scanning technology is said to reduce hacking by 75% and is an emerging trend being used abroad.
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ACEP Magazine edition 4th launched on 05.06.2024
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
bank management system in java and mysql report1.pdf
truth is high but higher still is truth full living
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Bank Locker Face Recognition.ppt
- 1. Banking Locker Security Arpana Shree Department of MCA Mount Carmel College Bangalore
- 2. Agenda • Draw back of current technology • Problem analysis model • About hidden image scanner • Algorithm overview • Conclusion
- 3. What goes wrong??? • Theft can take variety of forms - Insider - Outsider
- 5. Problem Domain • Draw Back of alarm & CCTV technology - Locker not secured • Draw Back of RFID technology - Passwords Hacking
- 7. Analyzing the problem • Usage of security tokens – Hardware/software • Usage of 2FA • Image scanning technology - Current trend being implemented
- 9. Image scanning technology • Uses a scanner • Hidden scanner is being fixed • Theft may not be aware of the hidden Scanner • Face detection algorithm is used
- 10. Example Tiger
- 11. Face detection algorithm Cb Part of the input image (fig-1) Cr Part of the input image (fig-2)
- 12. The likely hood of the skin regions (fig-3) Likelihood=exp[-0.5(X-M) c^-1 (X-M)] where X=P(r,b)^T M=Mean value of X C=Covariance of X
- 13. Image after thresholding (fig-4) Using adaptive threshold the Skin regions are highlighted effectively
- 14. Template face used to verify the existence of face in skin region (fig-5) Face boundary for the given image (fig-6)
- 15. Continued • The detected face is given as a input as a dimension 100X100 to Eigen face recognition system. • The output will be of the dimension 100X1
- 16. Conclusion • Image scanner implementation in banking locker security reduces 75% of hacking • One of the emerging trend in IT industry • Implemented in abroad Countries
- 17. THANK YOU!!!