Local classifier chains for deep face recognition
Lingfeng Zhang, Ioannis A. Kakadiaris · 2017
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.