Citation

Leong, Shu Min and Phan, Raphael Chung Wei and Baskaran, Vishnu Monn and Ooi, Chee Pun (2020) Privacy-preserving facial recognition based on temporal features. Applied Soft Computing, 96. pp. 1-18. ISSN 1568-4946

	Text S1568494620306001 Restricted to Repository staff only Download (89kB)
	Text Privacy-preserving facial recognition based on temporal features.pdf Restricted to Repository staff only Download (2MB)

Abstract

This paper proposes a novel approach for privacy-preserving facial recognition based on the new feature computation technique: Local Binary Pattern from Temporal Planes (LBP-TP) that extracts information from only the or planes of a video sequence; in contrast to previous work that depend significantly on spatial information within the video frames. To our knowledge, this is the first known facial recognition work that does not rely on the spatial plane, nor that requires processing a facial input. The removal of this spatial reliance therefore withholds the facial appearance information from public view, where only one-dimensional spatial information that varies across time are extracted for recognition. Privacy is thus assured, yet without impeding the facial recognition task which is vital for many security applications such as street surveillance and perimeter access control. Experimental results indicate that the proposed method achieves accuracy of 99.56%, 98.19% and 100% for the recent CASME II, CAS(ME)2 and Honda/UCSD databases respectively. In addition, a 66% reduction in the number of bytes required for storage and recognition was also observed from these experiments. The outcomes of this research demonstrate that privacy in face recognition can be preserved, without compromising its security (i.e., recognition accuracy) and efficiency.

Item Type:	Article
Uncontrolled Keywords:	Human face recognition (computer science)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	19 Oct 2020 16:39
Last Modified:	19 Oct 2020 19:47
URII:	http://shdl.mmu.edu.my/id/eprint/7781

Downloads

Edit (login required)