تطبیق چهره و تشخیص زنده بودن مبتنی بر بازشناسی گفتار برای احراز هویت غیرحضوری
محورهای موضوعی : فناوری اطلاعات و ارتباطات
احمد دولت خواه
1
,
بهنام درستکار یاقوتی
2
,
راهب هاشم پور
3
1 - دانشگاه عالی دفاع ملی
2 - دانشگاه علوم انتظامی امین
3 - دانشگاه جامع علوم انتظامی امین
کلید واژه: احراز هویت غیرحضوری, بازشناسی چهره, تشخیص زنده بودن, بازشناسی گفتار,
چکیده مقاله :
با گسترش فناوری بسیاری از خدمات نهادها و سازمان‌ها به صورت الکترونیکی و هوشمند، در بستر اینترنت ارائه می‌گردد. پلیس نیز به عنوان یک نهاد ارائه‌دهنده خدمات به مردم و سایر نهادها، به دنبال هوشمندسازی خدمات خود می‌باشد. در همین راستا نیز سامانه‌های الکترونیکی و هوشمند مختلفی را ارائه کرده است. به دلیل عدم احراز هویت کاربران در این سامانه‌ها، بسیاری از خدماتی که می‌توانند به صورت غیرحضوری ارائه گردد، نیاز به مراجعه به دفاتر پلیس+۱۰ را دارند. محدودیت بودجه و تجهیزات برای پاسخگویی حضوری، محدودیت نیروهای پلیس و تمرکز آن‌ها بر روی موضوعات مهم، محدودیت تعداد دفاتر خدماتی در شهرستان‌ها و عدم دسترسی روستاها به این دفاتر، رشد روزافزون خدمات برخط و افزایش تقاضای مردم برای آن، به ویژه در شرایطی مانند بحران بیماری کرونا، سبب شده است تا نیاز به احراز هویت غیرحضوری بسیار مورد توجه قرار بگیرد. در این پژوهش، احراز هویت غیرحضوری و ضرورت استفاده از آن، روش‌های تشخیص زنده بودن و بازشناسی چهره که دو فناوری مهم در این حوزه است، مرور شده است. در ادامه یک روش کارآمد از مدل‌های یادگیری عمیق بازشناسی چهره برای تطبیق چهره و یک روش تشخیص زنده بودن تعاملی به وسیله‌ی بازشناسی گفتار فارسی ارائه شده است و در نهایت نتایج آزمایش این مدل‌ها بر روی داده‌های مربوط در این حوزه آورده شده است.
As technology develops, institutions and organizations provide many services electronically and intelligently over the Internet. The police, as an institution that provides services to people and other institutions, aims to make its services smarter. Various electronic and intelligent systems have been offered in this regard. Because these systems lack authentication, many services that can be provided online require a visit to +10 police stations. Budget and equipment limitations for face-to-face responses, limitations of the police force and their focus on essential issues, a lack of service offices in villages and a limited number of service offices in cities, and the growing demand for online services, especially in crisis situations like Corona disease, electronic authentication is becoming increasingly important. This article reviews electronic authentication and its necessity, liveness detection methods and face recognition which are two of the most important technologies in this area. In the following, we present an efficient method of face recognition using deep learning models for face matching, as well as an interactive liveness detection method based on Persian speech recognition. A final section of the paper presents the results of testing these models on relevant data from this field.
[1] Givens, G. H., Beveridge, J. R., Phillips, P. J., Draper, B., Lui, Y. M., and Bolme, D., “Introduction to face recognition and evaluation of algorithm performance,” Comput. Stat. Data Anal., vol. 67, pp. 236–247, 2013.
[2] FRANCIS GALTON, “Personal Identification and Description 2,” Nature, vol. 38, pp. 173–177, 1888.
[3] Hazim Barnouti, N., Sameer Mahmood Al-Dabbagh, S., and Esam Matti, W., “Face Recognition: A Literature Review,” Int. J. Appl. Inf. Syst., vol. 11, no. 4, pp. 21–31, 2016.
[4] Ding, X. and Fang, C., “Discussions on some problems in face recognition,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3338, pp. 47–56, 2004.
[5] Ahmad Tolba,Ali El-Baz, A. A. E.-H., “Face Recognition: A Literature Review,” Int. J. Appl. Inf. Syst., vol. 11, no. 4, pp. 21–31, 2016.
[6] Heisele, B., Ho, P., and Poggio, T., “Face recognition with support vector machines: Global versus component-based approach,” in Proceedings of the IEEE International Conference on Computer Vision, 2001, vol. 2, pp. 688–694.
[7] Adjabi, I., Ouahabi, A., Benzaoui, A., and Taleb-Ahmed, A., “Past, present, and future of face recognition: A review,” Electron., vol. 9, no. 8, pp. 1–53, 2020.
[8] matthew a.turk, A. p. pentlan., “Face recognition using eigenfaces,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991.
[9] Sharif, M., Naz, F., Yasmin, M., Shahid, M. A., and Rehman, A., “Face recognition: A survey,” J. Eng. Sci. Technol. Rev., vol. 10, no. 2, pp. 166–177, 2017.
[10] Liu, C. and Wechsler, H., “Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition,” IEEE Trans. Image Process., vol. 11, no. 4, pp. 467–476, 2002.
[11] Taigman, Y., Yang, M., Ranzato, M., and Wolf, L., “DeepFace: Closing the gap to human-level performance in face verification,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 1701–1708, 2014.
[12] Sun, Y., Chen, Y., Wang, X., and Tang, X., “Deep learning face representation by joint identification-verification,” Adv. Neural Inf. Process. Syst., vol. 3, no. January, pp. 1988–1996, 2014.
[13] Parkhi, O. M., Vedaldi, A., and Zisserman, A., “Deep Face Recognition - Abstract only,” Procedings Br. Mach. Vis. Conf. 2015, no. Section 3, pp. 41.1-41.12, 2015.
[14] Cao, Q., Shen, L., Xie, W., Parkhi, O. M., and Zisserman, A., “VGGFace2: A dataset for recognising faces across pose and age,” Proc. - 13th IEEE Int. Conf. Autom. Face Gesture Recognition, FG 2018, no. May, pp. 67–74, 2018.
[15] Schroff, F., Kalenichenko, D., and Philbin, J., “FaceNet: A unified embedding for face recognition and clustering,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June-2015, pp. 815–823, 2015.
[16] Samir, C. et al., “An Intrinsic Framework for Analysis of Facial Surfaces To cite this version : HAL Id : hal-00665862 An Intrinsic Framework for Analysis of Facial Surfaces,” pp. 80–95, 2012.
[17] Deng, J., Guo, J., Liu, T., Gong, M., and Zafeiriou, S., “Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12356 LNCS, pp. 741–757, 2020.
[18] Deng, J., Guo, J., Yang, J., Xue, N., Kotsia, I., and Zafeiriou, S., “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 10, pp. 5962–5979, 2022.
[19] Bowyer, K. W., Chang, K., and Flynn, P., “A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition,” Comput. Vis. Image Underst., vol. 101, no. 1, pp. 1–15, 2006.
[20] Li, X., Jia, T., and Zhang, H., “Expression-insensitive 3D face recognition using sparse representation,” 2009 IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2009, pp. 2575–2582, 2009.
[21] Drira, H., Ben Amor, B., Srivastava, A., Daoudi, M., and Slama, R., “3D Face recognition under expressions, occlusions, and pose variations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2270–2283, 2013.
[22] Gupta, S., Markey, M. K., and Bovik, A. C., “Anthropometric 3D face recognition,” Int. J. Comput. Vis., vol. 90, no. 3, pp. 331–349, 2010.
[23] Koudelka, M. L., Koch, M. W., and Russ, T. D., “A prescreener for 3D face recognition using radial symmetry and the Hausdorff fraction,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2005-September, pp. 1–8, 2005.
[24] Cover, T. M. and Hart, P. E., “Nearest Neighbor Pattern Classification,” IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, 1967.
[25] Tang, H., Yin, B., Sun, Y., and Hu, Y., “3D face recognition using local binary patterns,” Signal Processing, vol. 93, no. 8, pp. 2190–2198, 2013.
[26] Lei, Y., Bennamoun, M., and El-Sallam, A. A., “An efficient 3D face recognition approach based on the fusion of novel local low-level features,” Pattern Recognit., vol. 46, no. 1, pp. 24–37, 2013.
[27] Berretti, S., Del Bimbo, A., and Pala, P., “3D face recognition using isogeodesic stripes,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 12, pp. 2162–2177, 2010.
[28] Chang, K. I., Bowyer, K. W., and Flynn, P. J., “Multiple nose region matching for 3D face recognition under varying facial expression,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 10, pp. 1695–1700, 2006.
[29] Newton, E., “Overview of the ISO / IEC 30107 Project Authentication Use Case Comparison,” pp. 1–13.
[30] Hernandez-Ortega, J., Fierrez, J., Morales, A., and Galbally, J., “Introduction to face presentation attack detection,” Adv. Comput. Vis. Pattern Recognit., no. April, pp. 187–206, 2019.
[31] Tseng, T. C., Shih, T. F., and Fuh, C. S., “Anti-spoofing of live face authentication on smartphone,” J. Inf. Sci. Eng., vol. 37, no. 3, pp. 605–616, 2021.
[32] Määttä, J., Hadid, A., and Pietikäinen, M., “Face spoofing detection from single images using texture and local shape analysis,” IET Biometrics, vol. 1, no. 1, pp. 3–10, 2012.
[33] Agarwal, A., Singh, R., and Vatsa, M., “Face anti-spoofing using Haralick features,” 2016 IEEE 8th Int. Conf. Biometrics Theory, Appl. Syst. BTAS 2016, no. September, 2016.
[34] Yang, J., Lei, Z., Liao, S., and Li, S. Z., “Face liveness detection with component dependent descriptor,” Proc. - 2013 Int. Conf. Biometrics, ICB 2013, 2013.
[35] He, J. and Luo, J., “Face Spoofing Detection Based on Combining Different Color Space Models,” 2019 IEEE 4th Int. Conf. Image, Vis. Comput. ICIVC 2019, pp. 523–528, 2019.
[36] Mahore, A. and Tripathi, M., “Detection of 3D Mask in 2D face recognition system using DWT and LBP,” 2018 IEEE 3rd Int. Conf. Commun. Inf. Syst. ICCIS 2018, pp. 18–22, 2019.
[37] Uzun, E., Chung, S. P. H., Essa, I., and Lee, W., “rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System,” pp. 1–15, 2018.
[38] Zhang, Y. et al., “CelebA-Spoof: Large-Scale Face Anti-spoofing Dataset with Rich Annotations,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12357 LNCS, pp. 70–85, 2020.
[39] Duta, I. C., Liu, L., Zhu, F., and Shao, L., “Improved residual networks for image and video recognition,” Proc. - Int. Conf. Pattern Recognit., pp. 9415–9422, 2020.
