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Local classifier chains for deep face recognition | IEEE Conference Publication | IEEE Xplore

Local classifier chains for deep face recognition


Abstract:

This paper focuses on improving the performance of current convolutional neural networks in face recognition without changing the network architecture. We propose a hiera...Show More

Abstract:

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.
Date of Conference: 01-04 October 2017
Date Added to IEEE Xplore: 01 February 2018
ISBN Information:
Electronic ISSN: 2474-9699
Conference Location: Denver, CO, USA

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