Deep ear biometrics

Axel-Christian Guei; Moulay A. Akhloufi

doi:10.1117/12.2304814

9 May 2018 Deep ear biometrics

Proceedings Volume 10652, Disruptive Technologies in Information Sciences; 106520Q (2018) https://doi.org/10.1117/12.2304814
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Ear biometrics has known an increase of interest from the computer vision research community in recent years. This is mainly because ear geometric features can be extracted in a non-intrusive way, are unique to each individual and does not change over time. Different techniques were proposed to extract ear features in 2D and 3D space and use them in a person recognition system. In this work, we propose Deep-Ear a deep convolutional residual network to perform ear recognition. The proposed algorithm uses a 50 layers deep residual network (ResNet50) as a feature extractor followed by 2 fully connected layers and a final softmax layer for classification. Experimental tests were performed on AMI-DB ear dataset. The obtained top-1 accuracy is equal to 95.67% and a top-3 accuracy is 99.67%. These results show that the proposed architecture is promising in developing a robust feature-free ear recognition technique based on deep learning.

Conference Presentation

Citation Download Citation

Axel-Christian Guei and Moulay A. Akhloufi "Deep ear biometrics", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520Q (9 May 2018); https://doi.org/10.1117/12.2304814

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