Employing fusion of learned and handcrafted features for unconstrained ear recognition
Author:
Affiliation:
1. Computer Science and EngineeringUniversity of South Florida4202 E. Fowler Avenue, ENB 118TampaFL33620USA
2. Department of Computer ScienceFederal University of BahiaAvenue Adhemar de Barros S/N, IM 214SalvadorBA40170‐110Brazil
Funder
Division of Computer and Network Systems
Publisher
Institution of Engineering and Technology (IET)
Subject
Computer Vision and Pattern Recognition,Signal Processing,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2017.0210
Reference27 articles.
1. Comparison and combination of ear and face images in appearance-based biometrics
2. Automated human identification using ear imaging
3. Yan P. Bowyer K.: ‘Empirical evaluation of advanced ear biometrics’.IEEE Conf. Computer Vision and Pattern Recognition Workshops 2005 pp.41–41
4. Ear recognition: More than a survey
5. Emersic Z. Stepec D. Struc V. et al. : ‘The unconstrained ear recognition challenge’.Int. Joint Conf. Biometrics 2017
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