TY - GEN
T1 - PCA filter based covariance descriptor for 2.5D face recognition
AU - Chong, Lee Ying
AU - Teoh, Andrew Beng Jin
AU - Ong, Thian Song
PY - 2017/4/21
Y1 - 2017/4/21
N2 - Region covariance matrix (RCM) as a feature descriptor is shown promising in various object detection and recognition tasks. However, vanilla RCM breaks down in face recognition due to its inadequacy in extracting discriminative features from facial image. In this paper, cascaded Principle Component Analysis (PCA) filter responses that derived from the multi-layer PCA network are leveraged to extract the sufficient discriminative facial feature for RCM construction. The factors that affect the performance of cascaded PCA filter responses in forming RCM for 2.5D face recognition is investigated. To be specific, the influence of patch size and filter numbers of cascaded PCA filter responses to RCM is probed. Besides that, block division is proposed for RCM to further enhance the accuracy performance. Experimental results have demonstrated the efficacy of the proposed approach.
AB - Region covariance matrix (RCM) as a feature descriptor is shown promising in various object detection and recognition tasks. However, vanilla RCM breaks down in face recognition due to its inadequacy in extracting discriminative features from facial image. In this paper, cascaded Principle Component Analysis (PCA) filter responses that derived from the multi-layer PCA network are leveraged to extract the sufficient discriminative facial feature for RCM construction. The factors that affect the performance of cascaded PCA filter responses in forming RCM for 2.5D face recognition is investigated. To be specific, the influence of patch size and filter numbers of cascaded PCA filter responses to RCM is probed. Besides that, block division is proposed for RCM to further enhance the accuracy performance. Experimental results have demonstrated the efficacy of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=85021442553&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021442553&partnerID=8YFLogxK
U2 - 10.1145/3077829.3077832
DO - 10.1145/3077829.3077832
M3 - Conference contribution
AN - SCOPUS:85021442553
T3 - ACM International Conference Proceeding Series
SP - 13
EP - 20
BT - Proceedings of 2017 International Conference on Biometrics Engineering and Application, ICBEA 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Biometrics Engineering and Application, ICBEA 2017
Y2 - 21 April 2017 through 23 April 2017
ER -