Title:	An improved simple feature set for face presentation attack detection
Authors:	Denisova, Anna
Citation:	WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p.
Issue Date:	2022
Publisher:	Václav Skala - UNION Agency
Document type:	conferenceObject
URI:	http://hdl.handle.net/11025/49574
ISBN:	978-80-86943-33-6
ISSN:	2464-4617
Keywords:	prezentace detekce útoku;extrakce funkcí;hloubková mapu;tepelná data;infračervená data;WMCA;SVM;RDWT- Haralick-SVM;MC-CNN
Keywords in different language:	presentation attack detection;feature extraction;depth map;thermal data;infrared data;WMCA;SVM;RDWT- Haralick-SVM;MC-CNN
Abstract in different language:	Presentation attacks are weak points of facial biometrical authentication systems. Although several presentation attack detection methods were developed, the best of them require a sufficient amount of training data and rely on computationally intensive deep learning based features. Thus, most of them have difficulties with adaptation to new types of presentation attacks or new cameras. In this paper, we introduce a method for face presentation attack detection with low requirements for training data and high efficiency for a wide range of spoofing attacks. The method includes feature extraction and binary classification stages. We use a combination of simple statistical and texture features and describe the experimental results of feature adjustment and selection. We validate the proposed method using WMCA dataset. The experiments showed that the proposed features decrease the average classification error in comparison with the RDWT-Haralick-SVM method and demonstrate the best performance among non-CNN-based methods
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2022: Full Papers Proceedings

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