Computer Science and Information Systems 2024 Volume 21, Issue 4, Pages: 1359-1387
https://doi.org/10.2298/CSIS240229038B
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Biometric lock with facial recognition implemented with deep learning techniques
Burruel-Zazueta José Misael (Tecnologico Nacional de Mexico/Instituto Tecnologico, Guadalupe, Culiacan Rosales, Sin., Mexico), d13170395@culiacan.tecnm.mx
Rodriguez-Rangel Hector (Tecnologico Nacional de Mexico/Instituto Tecnologico, Guadalupe, Culiacan Rosales, Sin., Mexico), hector.rr@culiacan.tecnm.mx
Peralta-Penunuri Gloria Ekaterine (Tecnologico Nacional de Mexico/Instituto Tecnologico, Guadalupe, Culiacan Rosales, Sin., Mexico), vgloria.pp@culiacan.tecnm.mx
Cayuela Vicenç Puig (Institut de Robotica i Informatica Industrial, CSIC-UPC, Barcelona, Espana), vicenc.puig@upc.edu
Algredo-Badillo Ignacio (CONACYT-Instituto Nacional de Astrofisica, Optica y Electronica Tonantzintla, Puebla, Mexico), algredobadillo@inaoep.mx
Morales-Rosales Luis Alberto (CONACYT-Universidad Michoacana de San Nicolas de Hidalgo, Ciudad Universitaria, Morelia, Mexico), lamorales@conacyt.mx
The increased criminal activity associated with unauthorized entry into facilities has become a global concern. Traditional mechanical locks suffer from drawbacks such as key loss, theft, duplication risks, and time-consuming operation. Therefore, biometrics has been explored as a key to accessing a restricted area. However, some challenges still need to be solved in developing such systems, including user registration, response speed, maintainability, and the ability to distinguish between real and fake individuals. This paper proposes and develops a biometric lock system (BLS) whose opening is performed by recognizing a person’s face. It solves the challenges of re-training, antispoofing, real-time response, and works inside an embedding system. The proposed BLS overcomes these challenges using a pre-trained network called FaceNet for feature extraction and coding into 128-dimensional vectors.We use the characteristic vector provided by FaceNet and a cosine distance to recognize the persons. It also incorporates ResNet18 + remote photoplethysmography (rPPG) to avoid spoofing. The architecture was implemented in a BLS, demonstrating an impressive false acceptance rate of 0% under varying lighting conditions, with an average response time of 1.68 seconds from facial detection to door opening. The BLS has easy maintainable devices, providing enhanced security by accurately identifying individuals and preventing unauthorized access. The system can distinguish between real and fake people without using specialized hardware. Making it a versatile solution suitable for homes, offices, and commercial spaces. The results underscore the potential efficacy of our proposed BLS in mitigating security challenges related to unwarranted access to restricted facilities.
Keywords: FaceNet, Jetson Nano, CNN, Door lock, Embedded system
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Ali,W., Tian,W., Din, S.U., Iradukunda, D., Khan, A.A.: Classical and modern face recognition approaches: a complete review. Multimedia tools and applications 80, 4825-4880 (2021)
Arashloo, S.R., Kittler, J.: Efficient processing of mrfs for unconstrained-pose face recognition. In: 2013 IEEE sixth international conference on biometrics: theory, applications and systems (BTAS). pp. 1-8. IEEE (2013)
ArduCam: Camera sony imx219, https://www.arducam.com/product/arducam-raspberry-pi-camera-v2-8mp-ixm219-b0103/, web accessed in: 07-06-2022
Arduino: Arduino uno r3, https://docs.arduino.cc/hardware/uno-rev3, web accessed in: 07-06-2022
Augusto, J.C., McCullagh, P.: Ambient intelligence: Concepts and applications. Computer Science and Information Systems 4(1), 1-27 (2007)
Bhatt, H.S., Bharadwaj, S., Singh, R., Vatsa, M.: Recognizing surgically altered face images using multiobjective evolutionary algorithm. IEEE Transactions on Information Forensics and Security 8(1), 89-100 (2012)
Bud, A.: Facing the future: The impact of apple faceid. Biometric technology today 2018(1), 5-7 (2018)
Chen, S., Shen, J., You, X., Chen, J., Yu, C.: A dynamic cryptography door lock system based on visible light communication. In: 2018 23rd Opto-Electronics and Communications Conference (OECC). pp. 1-2 (2018)
Cui, Z., Li, W., Xu, D., Shan, S., Chen, X.: Fusing robust face region descriptors via multiple metric learning for face recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3554-3561 (2013)
Das, P.K., Hu, B., Liu, C., Cui, K., Ranjan, P., Xiong, G.: A new approach for face anti-spoofing using handcrafted and deep network features. In: 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). pp. 33-38. IEEE (2019)
Ding, C., Tao, D.: Robust face recognition via multimodal deep face representation. IEEE transactions on Multimedia 17(11), 2049-2058 (2015)
Eloff, R., Engelbrecht, H.A., Kamper, H.: Multimodal one-shot learning of speech and images. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 8623-8627 (2019)
Gałka, J., Masior, M., Salasa, M.: Voice authentication embedded solution for secured access control. IEEE Transactions on Consumer Electronics 60(4), 653-661 (2014)
Ganjoo, R., Purohit, A.: Anti-spoofing door lock using face recognition and blink detection. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT). pp. 1090-1096. IEEE (2021)
Ganjoo, R., Purohit, A.: Anti-spoofing door lock using face recognition and blink detection. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT). pp. 1090-1096 (2021)
Ghorbani, M., Targhi, A.T., Dehshibi, M.M.: Hog and lbp: Towards a robust face recognition system. In: 2015 Tenth International Conference on Digital Information Management (ICDIM). pp. 138-141 (2015)
Gonzalez-Jimenez, D., Alba-Castro, J.L.: Toward pose-invariant 2-d face recognition through point distribution models and facial symmetry. IEEE Transactions on Information Forensics and Security 2(3), 413-429 (2007)
Goud, K.N., Sindhuri, K.: Enhanced Security for Smart Door Using Biometrics and OTP, pp. 517-526. Springer International Publishing, Cham (2022), https://doi.org/10.1007/ 978-3-030-96634-8_47
Grassi, P.A., Garcia, M.E., Fenton, J.F.: NIST special publication 800-63b: Digital identity guidelines. Tech. Rep. 800-63B, National Institute of Standards and Technology (June 2017), https://doi.org/10.6028/NIST.SP.800-63B
Hafez, S.F., Selim, M.M., Zayed, H.H.: 2d face recognition system based on selected gabor filters and linear discriminant analysis lda. arXiv preprint arXiv:1503.03741 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770-778 (2016)
Hearst, M., Dumais, S., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intelligent Systems and their Applications 13(4), 18-28 (1998)
Hemalatha, A., Gandhimathi, G.: Rfid, password and otp based door lock system using 8051 microcontroller. International Journal of Engineering Research and Technology 7(11), 1-6 (2019)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications (2017), https://arxiv.org/abs/1704.04861
Hsu, H., Lachenbruch, P.A.: Paired t test. Wiley StatsRef: statistics reference online (2014)
Irjanto, N.S., Surantha, N.: Home security system with face recognition based on convolutional neural network. (IJACSA) International Journal of Advanced Computer Science and Applications 11(11) (2020)
Jayamaha, R.G.M.M., Senadheera, M.R.R., Gamage, T.N.C., Weerasekara, K.D.P.B., Dissanayaka, G.A., Kodagoda, G.N.: Voizlock - human voice authentication system using hidden markov model. In: 2008 4th International Conference on Information and Automation for Sustainability. pp. 330-335 (2008)
Jovanović, B., Milenković, I., Bogićević Sretenović, M., Simić, D.: Extending identity management system with multimodal biometric authentication. Computer Science and Information Systems/ComSIS 13(2), 313-334 (2016)
Khowaja, S.A., Lee, S.L.: Hybrid and hierarchical fusion networks: a deep cross-modal learning architecture for action recognition. Neural Computing and Applications 32(14), 10423- 10434 (2020)
Khowaja, S.A., Lee, S.L.: Semantic image networks for human action recognition. International Journal of Computer Vision 128(2), 393-419 (2020)
Komol, M.M.R., Podder, A.K., Ali, M.N., Ansary, S.M.: Rfid and finger print based dual security system: A robust secured control to access through door lock operation. American Journal of Embedded Systems and Applications 6(1), 15-22 (2018)
Kortli, Y., Jridi, M., Al Falou, A., Atri, M.: Face recognition systems: A survey. Sensors 20(2) (2020), https://www.mdpi.com/1424-8220/20/2/342
Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Transactions on Information Forensics and Security 4(1), 98-110 (2009)
Lewandowska, M., Nowak, J.: Measuring pulse rate with a webcam. Journal of Medical Imaging and Health Informatics 2(1), 87-92 (2012)
Lindner, T., Wyrwal, D., Bialek, M., Nowak, P.: Face recognition system based on a singleboard computer. In: 2020 International Conference Mechatronic Systems and Materials (MSM). pp. 1-6 (2020)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision - ECCV 2016. pp. 21-37. Springer International Publishing, Cham (2016)
Mian, A., Bennamoun, M., Owens, R.: An efficient multimodal 2d-3d hybrid approach to automatic face recognition. IEEE transactions on pattern analysis and machine intelligence 29(11), 1927-1943 (2007)
Nortje, L.: Direct and indirect multimodal few-shot learning of speech and images. Ph.D. thesis, Stellenbosch: Stellenbosch University (2020)
NVIDIA: Jetson nano developer kit, https://developer.nvidia.com/embedded/jetson-nano-developer-kit, web; accessed in 01-29-2021
Organization, U.N.: Unodc burglary 2018. https://dataunodc.un.org/data/crime/burglary, accessed: 2022-03-23
Orna, G., Benitez, D.S., Perez, N.: A low-cost embedded facial recognition system for door access control using deep learning. In: 2020 IEEE ANDESCON. pp. 1-6 (2020)
Pacheco, J., Miranda, K.: Design of a low-cost nfc door lock for a smart home system. In: 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). pp. 1-5 (2020)
Patil, B., Mahajan, V., Suryawanshi, S., Pawar, M.: Automatic door lock system using pin on android phone. International Research Journal of Engineering and Technology (IRJET) 5(11), 1007-1011 (2018)
Pillai, J.K., Patel, V.M., Chellappa, R., Ratha, N.K.: Secure and robust iris recognition using random projections and sparse representations. IEEE transactions on pattern analysis and machine intelligence 33(9), 1877-1893 (2011)
Prasad, S., Govindan, V., Sathidevi, P.: Palmprint authentication using fusion of wavelet and contourlet features. Security and Communication Networks 4(5), 577-590 (2011)
Prity, S.A., Afrose, J., Hasan, M.: Rfid based smart door lock security system. American Journal of Sciences and Engineering Research E-ISSN-2348-703X 4(3) (2021)
Queirolo, C.C., Silva, L., Bellon, O.R., Segundo, M.P.: 3d face recognition using simulated annealing and the surface interpenetration measure. IEEE transactions on pattern analysis and machine intelligence 32(2), 206-219 (2009)
Quinlan, J.R.: Improved use of continuous attributes in c4. 5. Journal of artificial intelligence research 4, 77-90 (1996)
Ranjan, R., Sankaranarayanan, S., Castillo, C.D., Chellappa, R.: An all-in-one convolutional neural network for face analysis. In: 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017). pp. 17-24 (2017)
Ren, J., Jiang, X., Yuan, J.: Relaxed local ternary pattern for face recognition. In: 2013 IEEE international conference on image processing. pp. 3680-3684. IEEE (2013)
Saputra, R., Surantha, N.: Smart and real-time door lock system for an elderly user based on face recognition. Bulletin of Electrical Engineering and Informatics 10(3), 1345-1355 (2021), https://beei.org/index.php/EEI/article/view/2955
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2015), http://dx.doi.org/10.1109/CVPR.2015.7298682
Serengil, S.I., Ozpinar, A.: Lightface: A hybrid deep face recognition framework. In: 2020 Innovations in Intelligent Systems and Applications Conference (ASYU). pp. 1-5 (2020)
Sidiropoulos, G.K., Kiratsa, P., Chatzipetrou, P., Papakostas, G.A.: Feature extraction for finger-vein-based identity recognition. Journal of Imaging 7(5), 89 (2021)
Song, K.C., Yan, Y.H., Chen, W.H., Zhang, X.: Research and perspective on local binary pattern. Acta Automatica Sinica 39(6), 730-744 (2013), https://www.sciencedirect.com/science/article/pii/S1874102913600518
Tang, C., Lu, J., Liu, J.: Non-contact heart rate monitoring by combining convolutional neural network skin detection and remote photoplethysmography via a low-cost camera. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Workshops (June 2018)
Thavalengal, S., Andorko, I., Drimbarean, A., Bigioi, P., Corcoran, P.: Proof-of-concept and evaluation of a dual function visible/nir camera for iris authentication in smartphones. IEEE Transactions on Consumer Electronics 61(2), 137-143 (2015)
Thavalengal, S., Bigioi, P., Corcoran, P.: Iris authentication in handheld devices-considerations for constraint-free acquisition. IEEE Transactions on Consumer Electronics 61(2), 245-253 (2015)
Wang, W., Den Brinker, A.C., Stuijk, S., De Haan, G.: Algorithmic principles of remote ppg. IEEE Transactions on Biomedical Engineering 64(7), 1479-1491 (2016)
Waseem, M., Khowaja, S.A., Ayyasamy, R.K., Bashir, F.: Face recognition for smart door lock system using hierarchical network. In: 2020 International Conference on Computational Intelligence (ICCI). pp. 51-56 (2020)
William, I., Ignatius Moses Setiadi, D.R., Rachmawanto, E.H., Santoso, H.A., Sari, C.A.: Face recognition using facenet (survey, performance test, and comparison). In: 2019 Fourth International Conference on Informatics and Computing (ICIC). pp. 1-6 (2019)
Wimmer, G., Prommegger, B., Uhl, A.: Finger vein recognition and intra-subject similarity evaluation of finger veins using the cnn triplet loss. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 400-406 (2021)
Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: CVPR 2011. pp. 529-534. IEEE (2011)
Xi, S., Yang, L., Zhao, Y., et al.: A practical design for face recognition with anti-spoofing based on non-visible light cameras. Academic Journal of Computing & Information Science 3(2) (2020)
Yan, Z., Zhao, S.: A usable authentication system based on personal voice challenge. In: 2016 International Conference on Advanced Cloud and Big Data (CBD). pp. 194-199. IEEE (2016)
Yang, W., Hu, J., Wang, S.: A delaunay quadrangle-based fingerprint authentication system with template protection using topology code for local registration and security enhancement. IEEE transactions on Information Forensics and Security 9(7), 1179-1192 (2014)