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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Face Anti-Spoofing With Deep Neural Network Distillation

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Author(s):
Li, Haoliang [1] ; Wang, Shiqi [2, 3] ; He, Peisong [4] ; Rocha, Anderson [5]
Total Authors: 4
Affiliation:
[1] Nanyang Technol Univ, Rapid Rich Object Search Lab, Singapore 639798 - Singapore
[2] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong - Peoples R China
[3] City Univ Hong Kong, Shenzhen Inst, Shenzhen 518057 - Peoples R China
[4] Sichuan Univ, Coll Cybersecur, Chengdu 610065 - Peoples R China
[5] Univ Estadual Campinas, Inst Comp, BR-13084851 Campinas - Brazil
Total Affiliations: 5
Document type: Journal article
Source: IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING; v. 14, n. 5, p. 933-946, AUG 2020.
Web of Science Citations: 0
Abstract

One challenging aspect in face anti-spoofing (or presentation attack detection, PAD) refers to the difficulty of collecting enough and representative attack samples for an application-specific environment. In view of this, we tackle the problem of training a robust PAD model with limited data in an application-specific domain. We propose to leverage data from a richer and related domain to learn meaningful features through the concept of neural network distilling. We first train a deep neural network based on reasonably sufficient labeled data in an attempt to ``teach{''} a neural network for the application-specific domain for which training samples are scarce. Subsequently, we form training sample pairs from both domains and formulate a novel optimization function by considering the cross-entropy loss, as well as maximum mean discrepancy of features and paired sample similarity embedding for network distillation. Thus, we expect to capture spoofing-specific information and train a discriminative deep neural network on the application-specific domain. Extensive experiments validate the effectiveness of the proposed scheme in face anti-spoofing setups. (AU)

FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
Grantee:Anderson de Rezende Rocha
Support Opportunities: Research Projects - Thematic Grants