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Deep Biometrics, 2020
- Richard Jiang

, Chang-Tsun Li
, Danny Crookes, Weizhi Meng
, Christophe Rosenberger:
Deep Biometrics. Unsupervised and Semi-Supervised Learning, Springer International Publishing 2020, ISBN 978-3-030-32583-1 - Haoyi Wang, Victor Sanchez, Wanli Ouyang, Chang-Tsun Li

:
Using Age Information as a Soft Biometric Trait for Face Image Analysis. 1-20 - Shan Lin, Chang-Tsun Li

:
Person Re-identification with Soft Biometrics Through Deep Learning. 21-36 - Gary Storey, Ahmed Bouridane, Richard Jiang

, Chang-Tsun Li
:
Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis. 37-49 - Wael Elloumi, Aladine Chetouani, Tarek Ben Charrada, Emna Fourati:

Anti-Spoofing in Face Recognition: Deep Learning and Image Quality Assessment-Based Approaches. 51-69 - Natalya Selitskaya, Stanislaw Sielicki

, Livia Jakaite, Vitaly Schetinin, F. Evans, Marc Conrad, Paul Sant:
Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets. 71-97 - Arindam Chaudhuri:

Deep Learning Models for Face Recognition: A Comparative Analysis. 99-140 - Zhi Lin, Weizhi Meng

, Wenjuan Li, Duncan S. Wong:
Developing Cloud-Based Intelligent Touch Behavioral Authentication on Mobile Phones. 141-159 - Dejan Stepec, Ziga Emersic, Peter Peer, Vitomir Struc:

Constellation-Based Deep Ear Recognition. 161-190 - Hiren J. Galiyawala, Mehul S. Raval, Anand Laddha:

Person Retrieval in Surveillance Videos Using Deep Soft Biometrics. 191-214 - Rumaisah Munir, Rizwan Ahmed Khan

:
Deep Spectral Biometrics: Overview and Open Issues. 215-243 - Bing Xu, Tobechukwu Agbele, Richard Jiang:

Biometric Blockchain: A Secure Solution for Intelligent Vehicle Data Sharing. 245-256 - Stefano Savian, Mehdi Elahi, Tammam Tillo:

Optical Flow Estimation with Deep Learning, a Survey on Recent Advances. 257-287 - Luís A. M. Pereira, Allan Pinto

, Fernanda A. Andaló, Alexandre M. Ferreira, Bahram Lavi, Aurea Soriano-Vargas
, Marcos V. M. Cirne, Anderson Rocha:
The Rise of Data-Driven Models in Presentation Attack Detection. 289-311

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