TY - GEN
T1 - Joint regularized nearest points for image set based face recognition
AU - Yang, Meng
AU - Liu, Weiyang
AU - Shen, Linlin
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/17
Y1 - 2015/7/17
N2 - Face recognition based on image set has attracted much attention due to its promising performance to overcome various variations. Recently, (collaborative) regularized nearest points (C)RNP has achieved the state-of-art performance by measuring the between-set distance as the distance between nearest points generated in each image set. However, the nearest point of the query set in RNP changes in computing its distance to nearest points of different gallery image sets, which may result in that a wrong gallery image set can also has a small between-set distance; CRNP used collaborative representation to overcome this issue but it doesn't explicitly minimize the between-set distance. In order to solve these issues and fully exploit the advantages of nearest point based approaches, in this paper a novel joint regularized nearest points (JRNP) is proposed for face recognition based on image sets. In JRNP, the nearest point in the query set keeps the same when computing its distance to the image sets of different classes; at the same time, it explicitly minimize the between-set distance of facial images. An efficient algorithm was proposed to solve this problem, and the classification is then based on the joint distance between the regularized nearest points in image sets. Extensive experiments on benchmark databases were conducted on benchmark databases (e.g., Honda/UCSD, CMU Mobo, and YouTube databases). The experimental results clearly show that our JRNP leads the performance in face recognition based on image sets.
AB - Face recognition based on image set has attracted much attention due to its promising performance to overcome various variations. Recently, (collaborative) regularized nearest points (C)RNP has achieved the state-of-art performance by measuring the between-set distance as the distance between nearest points generated in each image set. However, the nearest point of the query set in RNP changes in computing its distance to nearest points of different gallery image sets, which may result in that a wrong gallery image set can also has a small between-set distance; CRNP used collaborative representation to overcome this issue but it doesn't explicitly minimize the between-set distance. In order to solve these issues and fully exploit the advantages of nearest point based approaches, in this paper a novel joint regularized nearest points (JRNP) is proposed for face recognition based on image sets. In JRNP, the nearest point in the query set keeps the same when computing its distance to the image sets of different classes; at the same time, it explicitly minimize the between-set distance of facial images. An efficient algorithm was proposed to solve this problem, and the classification is then based on the joint distance between the regularized nearest points in image sets. Extensive experiments on benchmark databases were conducted on benchmark databases (e.g., Honda/UCSD, CMU Mobo, and YouTube databases). The experimental results clearly show that our JRNP leads the performance in face recognition based on image sets.
UR - http://www.scopus.com/inward/record.url?scp=84944936785&partnerID=8YFLogxK
U2 - 10.1109/FG.2015.7163108
DO - 10.1109/FG.2015.7163108
M3 - Conference contribution
AN - SCOPUS:84944936785
T3 - 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
BT - 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
Y2 - 4 May 2015 through 8 May 2015
ER -