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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:37:35Z","timestamp":1732041455217},"reference-count":46,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Assistive Devices for empowering dis-Abled People through the robotic Technologies (ADAPT) project"},{"name":"INTERREG VA France (Channel) England Programme"},{"name":"European Regional Development Fund"},{"name":"European Regional Development Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Despite the high performances achieved using deep learning techniques in biometric systems, the inability to rationalise the decisions reached by such approaches is a significant drawback for the usability and security requirements of many applications. For Facial Biometric Presentation Attack Detection (PAD), deep learning approaches can provide good classification results but cannot answer the questions such as \u201cWhy did the system make this decision\u201d? To overcome this limitation, an explainable deep neural architecture for Facial Biometric Presentation Attack Detection is introduced in this paper. Both visual and verbal explanations are produced using the saliency maps from a Grad-CAM approach and the gradient from a Long-Short-Term-Memory (LSTM) network with a modified gate function. These explanations have also been used in the proposed framework as additional information to further improve the classification performance. The proposed framework utilises both spatial and temporal information to help the model focus on anomalous visual characteristics that indicate spoofing attacks. The performance of the proposed approach is evaluated using the CASIA-FA, Replay Attack, MSU-MFSD, and HKBU MARs datasets and indicates the effectiveness of the proposed method for improving performance and producing usable explanations.<\/jats:p>","DOI":"10.3390\/s22093365","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T02:20:06Z","timestamp":1651198806000},"page":"3365","source":"Crossref","is-referenced-by-count":3,"title":["An Attention-Guided Framework for Explainable Biometric Presentation Attack Detection"],"prefix":"10.3390","volume":"22","author":[{"given":"Shi","family":"Pan","sequence":"first","affiliation":[{"name":"School of Engineering, University of Kent, Canterbury CT2 7NT, UK"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8627-3429","authenticated-orcid":false,"given":"Sanaul","family":"Hoque","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Kent, Canterbury CT2 7NT, UK"}]},{"given":"Farzin","family":"Deravi","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Kent, Canterbury CT2 7NT, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3038924","article-title":"Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey","volume":"50","author":"Ramachandra","year":"2017","journal-title":"ACM Comput. 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