TY - GEN
T1 - Local classifier chains for deep face recognition
AU - Zhang, Lingfeng
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-BSH001. This grant is awarded to the Borders, Trade, and Immigration (BTI) Institute: A DHS Center of Excellence led by the University of Houston, and includes support for the project “Image and Video Person Identification in an Operational Environment: Phase I” awarded to the University of Houston. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - This paper focuses on improving the performance of current convolutional neural networks in face recognition without changing the network architecture. We propose a hierarchical framework that builds chains of local binary neural networks after one global neural network over all the class labels, Local Classifier Chains based Convolutional Neural Networks (LCC-CNN). Two different criteria based on a similarity matrix and confusion matrix are introduced to select binary label pairs to create local deep networks. To avoid error propagation, each testing sample travels through one global model and a local classifier chain to obtain its final prediction. The proposed framework has been evaluated with UHDB31 and CASIA-WebFace datasets. The experimental results indicate that our framework achieves better performance when compared with using only baseline methods as the global deep network. The accuracy is improved by 2.7% and 0.7% on the two datasets, respectively.
AB - This paper focuses on improving the performance of current convolutional neural networks in face recognition without changing the network architecture. We propose a hierarchical framework that builds chains of local binary neural networks after one global neural network over all the class labels, Local Classifier Chains based Convolutional Neural Networks (LCC-CNN). Two different criteria based on a similarity matrix and confusion matrix are introduced to select binary label pairs to create local deep networks. To avoid error propagation, each testing sample travels through one global model and a local classifier chain to obtain its final prediction. The proposed framework has been evaluated with UHDB31 and CASIA-WebFace datasets. The experimental results indicate that our framework achieves better performance when compared with using only baseline methods as the global deep network. The accuracy is improved by 2.7% and 0.7% on the two datasets, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85045569384&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045569384&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2017.8272694
DO - 10.1109/BTAS.2017.8272694
M3 - Conference contribution
AN - SCOPUS:85045569384
T3 - IEEE International Joint Conference on Biometrics, IJCB 2017
SP - 158
EP - 167
BT - IEEE International Joint Conference on Biometrics, IJCB 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Joint Conference on Biometrics, IJCB 2017
Y2 - 1 October 2017 through 4 October 2017
ER -