NDLI: Learning pairwise SVM on hierarchical deep features for ear recognition

Content Provider	IET Digital Library
Author	Omara, Ibrahim Wu, Xiaohe Zhang, Hongzhi Du, Yong Zuo, Wangmeng
Abstract	Convolutional neural networks (CNNs)-based deep features have been demonstrated with remarkable performance in various vision tasks, such as image classification and face verification. Compared with the hand-crafted descriptors, deep features exhibit more powerful representation ability. Typically, higher layer features contain more semantic information, while lower layer features can provide more low-level description. In addition, it turns out that the fusion of different layer features will lead to superior performance. Here, we propose a novel approach for human ear identification by combining hierarchical deep features. First, hierarchical deep features are extracted from ear images using CNN pre-trained on large-scale data set. To enhance the feature representation and reduce the high dimension of deep features, the discriminant correlation analysis (DCA) is adopted for fusing deep features from different layers for further improvement. Owing to the lack of ear images per person, the authors propose to transform the ear identification problem to the binary classification by composing pairwise samples and resolve it with the pairwise support vector machine (SVM). Experiments are conducted on four public databases: USTB I, USTB II, IIT Delhi I, and IIT Delhi II. The proposed method achieves promising recognition rate and exhibits decent performance compared with the state-of-the-art methods.
Starting Page	557
Ending Page	566
Page Count	10
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 6, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0087
Journal	IET Biometrics
Publisher Date	2018-02-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CNN Model Computer Vision Computer Vision And Image Processing Technique Convolution Convolutional Neural Network DCA Algorithm Discriminant Correlation Analysis Algorithm Ear Ear Image Ear Recognition Face Recognition Feature Extraction Feature Representation Feedforward Neural Network Hierarchical Deep Feature Image Classification Image Recognition Image Representation Knowledge Engineering Technique Layer Feature Learning in AI Learning Pairwise SVM Neural Computing Technique Pairwise Support Vector Machine Pattern Classification Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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