TY - GEN

T1 - Face recognition on smartphones via optimised sparse representation classification

AU - Shen, Yiran

AU - Hu, Wen

AU - Yang, Mingrui

AU - Wei, Bo

AU - Lucey, Simon

AU - Chou, Chun Tung

PY - 2014/4/17

Y1 - 2014/4/17

N2 - 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.

AB - 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.

KW - Face Recognition

KW - Smartphones

KW - Random Matrices

KW - Sparse Representation

KW - Android

KW - JavaCV/OpenCV

KW - Face Unlocking

U2 - 10.1109/IPSN.2014.6846756

DO - 10.1109/IPSN.2014.6846756

M3 - Conference contribution/Paper

SN - 9781479931460

SP - 237

EP - 248

BT - IPSN 2014: Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

PB - IEEE

T2 - IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

Y2 - 15 April 2014 through 17 April 2014

ER -