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Title: Accurate periocular recognition under less constrained environment using semantics-assisted convolutional neural network
Authors: Zhao, ZJ
Kumar, A 
Keywords: Periocular recognition
Deep learning
Convolution neural network
Training data augmentation
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information forensics and security, 2017, v. 12, no. 5, p. 1017-1030 How to cite?
Journal: IEEE transactions on information forensics and security 
Abstract: Accurate biometric identification under real environments is one of the most critical and challenging tasks to meet growing demand for higher security. This paper proposes a new framework to efficiently and accurately match periocular images that are automatically acquired under less-constrained environments. Our framework, referred to as semantics-assisted convolutional neural networks (SCNNs) in this paper, incorporates explicit semantic information to automatically recover comprehensive periocular features. This strategy enables superior matching accuracy with the usage of relatively smaller number of training samples, which is often an issue with several biometrics. Our reproducible experimental results on four different publicly available databases suggest that the SCNN-based periocular recognition approach can achieve outperforming results, both in achievable accuracy and matching time, for less-constrained periocular matching. Additional experimental results presented in this paper also indicate that the effectiveness of proposed SCNN architecture is not only limited to periocular recognition but it can also be useful for generalized image classification. Without increasing the volume of training data, the SCNN is able to automatically extract more discriminative features from the input data than a single CNN, therefore can consistently improve the recognition performance. The experimental results presented in this paper validate such an approach to enable faster and more accurate periocular recognition under less constrained environments.
ISSN: 1556-6013
EISSN: 1556-6021
DOI: 10.1109/TIFS.2016.2636093
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