Face recognition on smartphones via optimised Sparse Representation Classification

Yira Shen, Wen Hu, Mingru Yang, Bo Wei, Simon Lucey, Chun Tun Chou

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Face recognition is an element of many smartphone apps, e.g. face unlocking, people tagging and games. Sparse Representation Classification (SRC) is a state-of-the-art face recognition algorithm, which has been shown to outperform many classical face recognition algorithms in OpenCV. The success of SRC is due to its use of ℓ 1 optimisation, which makes SRC robust to noise and occlusions. Since ℓ 1 optimisation is computationally intensive, SRC uses random projection matrices to reduce the dimension of the ℓ 1 problem. However, random projection matrices do not give consistent classification accuracy. In this paper, we propose a method to optimise the projection matrix for ℓ 1 -based classification 1 . Our evaluations, based on publicly available databases and real experiment, show that face recognition based on the optimised projection matrix can be 5-17% more accurate than its random counterpart and OpenCV algorithms. Furthermore, the optimised projection matrix does not have to be re-calculated even if new faces are added to the training set. We implement the SRC with optimised projection matrix on Android smartphones and find that the computation of residuals in SRC is a severe bottleneck, taking up 85-90% of the computation time. To address this problem, we propose a method to compute the residuals approximately, which is 50 times faster but without sacrificing recognition accuracy. Lastly, we demonstrate the feasibility of our new algorithm by the implementation and evaluation of a new face unlocking app and show its robustness to variation to poses, facial expressions, lighting changes and occlusions.
    Original languageEnglish
    Title of host publication IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)978-1-4799-3147-7
    Publication statusPublished - 8 Jul 2014
    Event13th ACM/IEEE Conference on Information Processing in Sensor Networks - Berlin, Germany
    Duration: 15 Apr 201417 Apr 2014


    Conference13th ACM/IEEE Conference on Information Processing in Sensor Networks
    Abbreviated titleIPSN 2014


    Dive into the research topics of 'Face recognition on smartphones via optimised Sparse Representation Classification'. Together they form a unique fingerprint.

    Cite this