{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:29:26Z","timestamp":1760059766520,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In the digital era, face recognition technology has emerged as a promising solution for enhancing payment system security and convenience. This systematic literature review examines face recognition advancements in payment security following the PRISMA framework. From 219 initially identified articles, we selected 10 studies meeting our technical criteria. The findings reveal significant progress in deep learning approaches, multimodal feature integration, and transformer-based architectures. Current trends emphasize multimodal systems combining RGB with IR and depth data for sophisticated attack detection. Critical challenges remain in cross-dataset generalization, evaluation standardization, computational efficiency, and combating advanced threats including deepfakes. This review identifies technical limitations and provides direction for developing robust facial recognition technologies for widespread payment adoption.<\/jats:p>","DOI":"10.3390\/info16070581","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T06:03:13Z","timestamp":1751868193000},"page":"581","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Secure Face Recognition Payment Systems: A Systematic Literature Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5900-7040","authenticated-orcid":false,"given":"M. Haswin Anugrah","family":"Pratama","sequence":"first","affiliation":[{"name":"Artificial Intelligence Department, Bank Rakyat Indonesia, Jakarta 10210, Indonesia"},{"name":"School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9712-965X","authenticated-orcid":false,"given":"Achmad","family":"Rizal","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4829-3266","authenticated-orcid":false,"given":"Indrarini Dyah","family":"Irawati","sequence":"additional","affiliation":[{"name":"School of Applied Science, Telkom University, Bandung 40257, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"ref_1","unstructured":"Bank Indonesia (2025, May 15). Indonesia Payment Systems Blueprint 2025: Navigating the National Payment Systems in the Digital Era. Bank Indonesia, Available online: https:\/\/www.bi.go.id\/en\/publikasi\/kajian\/Documents\/Indonesia-Payment-Systems-Blueprint-2025.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"154360","DOI":"10.1109\/ACCESS.2019.2927705","article-title":"Factors Affecting the Use of Facial-Recognition Payment: An Example of Chinese Consumers","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101939","DOI":"10.1016\/j.tele.2023.101939","article-title":"Disentangling facial recognition payment service usage behavior: A trust perspective","volume":"77","author":"Li","year":"2023","journal-title":"Telemat. Inf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102155","DOI":"10.1016\/j.telpol.2021.102155","article-title":"Resistance to facial recognition payment in China: The influence of privacy-related factors","volume":"45","author":"Liu","year":"2021","journal-title":"Telecomm. 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Tools Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.procs.2018.10.427","article-title":"Techniques and Challenges of Face Recognition: A Critical Review","volume":"143","author":"Singh","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Adjabi, I., Ouahabi, A., Benzaoui, A., and Taleb-Ahmed, A. (2020). Past, Present, and Future of Face Recognition: A Review. Electronics, 9.","DOI":"10.20944\/preprints202007.0479.v1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.inffus.2021.04.015","article-title":"A review of state-of-the-art in Face Presentation Attack Detection: From early development to advanced deep learning and multi-modal fusion methods","volume":"75","author":"Abdullakutty","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Khairnar, S., Gite, S., Kotecha, K., and Thepade, S.D. (2023). Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions. Big Data Cogn. Comput., 7.","DOI":"10.3390\/bdcc7010037"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ming, Z., Visani, M., Luqman, M.M., and Burie, J.-C. (2020). A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices. J. Imaging, 6.","DOI":"10.3390\/jimaging6120139"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3687264","article-title":"Presentation Attack Detection: A Systematic Literature Review","volume":"57","author":"Pooshideh","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1007\/s00530-023-01070-5","article-title":"A survey on face presentation attack detection mechanisms: Hitherto and future perspectives","volume":"29","author":"Sharma","year":"2023","journal-title":"Multimed. Syst."},{"key":"ref_15","first-page":"5609","article-title":"Deep Learning for Face Anti-Spoofing: A Survey","volume":"45","author":"Yu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., and Altman, D.G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med., 6.","DOI":"10.1371\/journal.pmed.1000097"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xiao, J., Wang, W., Zhang, L., and Liu, H. (2024). A MobileFaceNet-Based Face Anti-Spoofing Algorithm for Low-Quality Images. Electronics, 13.","DOI":"10.3390\/electronics13142801"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MCE.2024.3434909","article-title":"Deep Learning-Based Face Forgery Detection for Facial Payment Systems","volume":"14","author":"Guo","year":"2024","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1007\/s11263-024-02055-1","article-title":"Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing","volume":"132","author":"Yu","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, K., Huang, M., Zhang, G., Yue, H., Zhang, G., and Qiao, Y. (2023, January 17\u201324). Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00678"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s40747-023-01160-x","article-title":"DIFLD: Domain invariant feature learning to detect low-quality compressed face forgery images","volume":"10","author":"Zou","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, C., Li, Z., Sun, J., and Li, R. (2023). Middle-shallow feature aggregation in multimodality for face anti-spoofing. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-36636-w"},{"key":"ref_23","unstructured":"Lu, H., Ouyang, W., Huang, H., Lu, J., Liu, R., Dong, J., and Xu, M. (2023). Face Anti-spoofing Based on Client Identity Information and Depth Map. Image and Graphics, Proceedings of the ICIG 2023, Nanjing, China, 22\u201324 September 2023, Springer. Lecture Notes in Computer Science."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lu, X., and Tian, Y. (2020). Heterogeneous Kernel Based Convolutional Neural Network for Face Liveness Detection. Bio-Inspired Computing: Theories and Applications, Proceedings of the BIC-TA 2019, Zhengzhou, China, 22\u201325 November 2019, Springer. Communications in Computer and Information Science.","DOI":"10.1007\/978-981-15-3415-7_32"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Parkin, A., and Grinchuk, O. (2019, January 16\u201317). Recognizing Multi-Modal Face Spoofing with Face Recognition Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00204"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, B., Li, X., Yu, Z., and Zhao, G. (2019, January 29\u201331). Face Liveness Detection by rPPG Features and Contextual Patch-Based CNN. Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications (ICBEA 2019), Stockholm, Sweden.","DOI":"10.1145\/3345336.3345345"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhou, J., Wang, Y., Sun, Z., Jia, Z., Feng, J., Shan, S., Ubul, K., and Guo, Z. (2018). MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices. Biometric Recognition, Proceedings of the CCBR 2018, Urumqi, China, 11\u201312 August 2018, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-97909-0"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yu, Z., Zhao, C., Wang, Z., Qin, Y., Su, Z., Li, X., Zhou, F., and Zhao, G. (2020, January 13\u201319). Searching Central Difference Convolutional Networks for Face Anti-Spoofing. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00534"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"R\u00f6ssler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Niessner, M. (November, January 27). FaceForensics++: Learning to Detect Manipulated Facial Images. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Repiblic of Korea.","DOI":"10.1109\/ICCV.2019.00009"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tan, X., Li, Y., Liu, J., and Jiang, L. (2010). Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model. Computer Vision\u2013ECCV 2010, Heraklion, Crete, Greece, 5\u201311 September 2010, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-642-15567-3_37"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., and Li, S.Z. (April, January 29). A Face Antispoofing Database with Diverse Attacks. Proceedings of the 2012 5th IAPR International Conference on Biometrics (ICB), New Delhi, India.","DOI":"10.1109\/ICB.2012.6199754"},{"key":"ref_33","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., and Girshick, R. (2022, January 18\u201324). Masked Autoencoders Are Scalable Vision Learners. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TIFS.2019.2916652","article-title":"Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network","volume":"15","author":"George","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/TBIOM.2020.2973001","article-title":"CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing","volume":"2","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1109\/TIFS.2021.3065495","article-title":"Face Anti-Spoofing via Adversarial Cross-Modality Translation","volume":"16","author":"Liu","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1109\/TIFS.2023.3337970","article-title":"Surveillance Face Anti-Spoofing","volume":"19","author":"Fang","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_41","unstructured":"Li, X., Komulainen, J., Zhao, G., Yuen, P.-C., and Pietik\u00e4inen, M. (2016, January 4\u20138). Generalized Face Anti-Spoofing by Detecting Pulse from Face Videos. Proceedings of the International Conference on Pattern Recognition (ICPR), Cancun, Mexico."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/TIFS.2015.2400395","article-title":"Face Spoof Detection with Image Distortion Analysis","volume":"10","author":"Wen","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/TIFS.2014.2322255","article-title":"Spoofing Face Recognition with 3D Masks","volume":"9","author":"Erdogmus","year":"2014","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, S., Yuen, P.C., Zhang, S., and Zhao, G. 3D Mask Face Anti-spoofing with Remote Photoplethysmography. Proceedings of the Computer Vision\u2013ECCV 2016, Amsterdam, The Netherlands, 11\u201314 October 2016, Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46478-7_6"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., and Hadid, A. (June, January 30). OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations. Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG), Washington, DC, USA.","DOI":"10.1109\/FG.2017.77"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s11760-016-1014-2","article-title":"Deep Face Liveness Detection Based on Nonlinear Diffusion Using Convolution Neural Network","volume":"11","author":"Alotaibi","year":"2017","journal-title":"Signal Image Video Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1109\/TIP.2015.2422574","article-title":"Face Liveness Detection From a Single Image via Diffusion Speed Model","volume":"24","author":"Kim","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hao, H., Pei, M., and Zhao, M. (2019). Face Liveness Detection Based on Client Identity Using Siamese Network. Pattern Recognition and Computer Vision, Proceedings of the PRCV 2019, Xi\u2019an, China, 8\u201311 November 2019, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-31654-9_15"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, Y., Jourabloo, A., and Liu, X. (2018, January 18\u201322). Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00048"},{"key":"ref_50","unstructured":"Chingovska, I., Anjos, A., and Marcel, S. (2012, January 6\u20137). On the Effectiveness of Local Binary Patterns in Face Anti-Spoofing. Proceedings of the 2012 International Conference of Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/581\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:05:34Z","timestamp":1760033134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,7]]},"references-count":51,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["info16070581"],"URL":"https:\/\/doi.org\/10.3390\/info16070581","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,7,7]]}}}