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A novel approach for ear recognition: learning Mahalanobis distance features from deep CNNs

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Abstract

Recently, deep convolutional neural networks (CNNs) have been used for ear recognition with the increasing and available ear image databases. However, most known ear recognition methods may be affected by selecting and weighting features; this is always a challenging issue in ear recognition and other pattern recognition applications. Metric learning can address this issue by using an accurate and efficient metric distance called Mahalanobis distance. Therefore, this paper presents a novel approach for ear recognition problems based on a learning Mahalanobis distance metric on deep CNN features. In detail, firstly, various deep features are extracted by adopting VGG and ResNet pre-trained models. Secondly, the discriminant correlation analysis is exploited to eliminate the dimensionality problem. Thirdly, the Mahalanobis distance is learned based on LogDet divergence metric learning. Finally, K-nearest neighbor is used for ear recognition. The experiments are performed on four public ear databases: AWE, USTB II, AMI, and WPUT, and experimental results prove that the proposed approach outperforms the existing state-of-the-art ear recognition methods.

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Acknowledgement

This work was supported in part by the cooperation between Higher Education Commission of Egypt and Chinese Government, the National Natural Science Foundation of China (No. 81671768), and the National Key R&D program of China (Grant No. 2017YFC0112804).

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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Ibrahim Omara, Guangzhi Ma & Enmin Song

  2. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Kom, 32511, Egypt

    Ibrahim Omara

  3. Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt

    Ahmed Hagag

  4. Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Fathi E. Abd El-Samie

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  1. Ibrahim Omara
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  2. Ahmed Hagag
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  4. Fathi E. Abd El-Samie
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  5. Enmin Song
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Correspondence to Guangzhi Ma.

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Omara, I., Hagag, A., Ma, G. et al. A novel approach for ear recognition: learning Mahalanobis distance features from deep CNNs. Machine Vision and Applications 32, 38 (2021). https://doi.org/10.1007/s00138-020-01155-5

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