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.
Axel-Christian Guei and Moulay A. Akhloufi, "Deep ear biometrics," Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520Q (Presented at SPIE Defense + Security: April 18, 2018; Published: 9 May 2018); https://doi.org/10.1117/12.2304814.
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