Face Anti-Spoofing Using Multi-Branch CNN

Nguyen Cong Tin, Bach Tung Pham, Thi Phuong Le, Tzu Chiang Tai, Jia Ching Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a face classification system based on deep learning algorithm. This system is capable of distinguishing real and fake faces from RGB images taken by a normal camera. To do that, we have built a system composed of 4 parts: RGB image processing, HSV image processing, YCrCb image processing, and classification. In order to achieve optimal processing performance, we include encoder and decoder structure models, which eliminate unnecessary components and help the model focus only on the components it gives. Most importantly, this structure helps reduce the complexity of the model. In addition, we have applied a number of special tweaks to the training data. Experimental results indicate that our system gives very good results on the public database.

Original languageEnglish
Title of host publication2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages170-173
Number of pages4
ISBN (Electronic)9789881476890
StatePublished - 2021
Event2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
Duration: 14 Dec 202117 Dec 2021

Publication series

Name2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Country/TerritoryJapan
CityTokyo
Period14/12/2117/12/21

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