TY - GEN
T1 - Local classifier chains for deep face recognition
AU - Zhang, Lingfeng
AU - Kakadiaris, Ioannis A.
N1 - 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 -