Abstract
Campus or institution security has been a prominent area of study in recent decades. Facial identification, voice verification, and vehicle license plate recognition have all been used individually in the literature to prevent attackers from entering the facility. Although several academics have agreed that a hybrid recognition system may significantly increase security, hybrid systems are not often discussed in the literature. To overcome this issue, we presented a hybrid driver and vehicle identification module in this study, which can detect both the driver and the vehicle. We applied face recognition and speech verification for driver recognition. Multi-task Cascaded Convolutional Networks were used to crop the faces for facial recognition, while FaceNet was employed for face identification. A three-layer Long Short-Term Memory model was used for voice verification. Finally, Tesseract has been used for car number plate identification. According to the results of the experiments, the suggested approach is capable of detecting both drivers and cars with 0 percent mistake every time, which is a critical improvement for assuring the security of the institutions.
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References
Al Awaimri, M., Fageeri, S., Moyaid, A., Thron, C., ALhasanat, A.: Automatic number plate recognition system for Oman. In: Alloghani, M., Thron, C., Subair, S. (eds.) Artificial Intelligence for Data Science in Theory and Practice. SCI, vol. 1006, pp. 155–178. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92245-0_8
Antar, R., Alghamdi, S., Alotaibi, J., Alghamdi, M.: Automatic number plate recognition of Saudi license car plates. Eng. Technol. Appl. Sci. Res. 12(2), 8266–8272 (2022)
Azadani, M.N., Boukerche, A.: Driverrep: driver identification through driving behavior embeddings. J. Parallel Distrib. Comput. 162, 105–117 (2022)
Bacchini, F., Lorusso, L.: Race, again: how face recognition technology reinforces racial discrimination. J. Inf. Commun. Ethics Soc. (2019)
Bimbot, F., et al.: A tutorial on text-independent speaker verification. EURASIP J. Adv. Signal Process. 2004(4), 1–22 (2004)
Boutros, F., Damer, N., Kirchbuchner, F., Kuijper, A.: Self-restrained triplet loss for accurate masked face recognition. Pattern Recogn. 124, 108473 (2022)
Cheng, J.M., Wang, H.C.: A method of estimating the equal error rate for automatic speaker verification. In: 2004 International Symposium on Chinese Spoken Language Processing, pp. 285–288. IEEE (2004)
Gnanaprakash, V., Kanthimathi, N., Saranya, N.: Automatic number plate recognition using deep learning. In: IOP Conference Series: Materials Science and Engineering, vol. 1084, p. 012027. IOP Publishing (2021)
Hammouche, R., Attia, A., Akhrouf, S., Akhtar, Z.: Gabor filter bank with deep autoencoder based face recognition system. Expert Syst. Appl., 116743 (2022)
Hofbauer, H., Uhl, A.: Calculating a boundary for the significance from the equal-error rate. In: 2016 International Conference on Biometrics (ICB), pp. 1–4. IEEE (2016)
Huang, B., et al.: Joint segmentation and identification feature learning for occlusion face recognition. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Huang, Z., Zhang, J., Shan, H.: When age-invariant face recognition meets face age synthesis: a multi-task learning framework. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7282–7291 (2021)
Ivanko, D., Ryumin, D., Axyonov, A., Kashevnik, A.: Speaker-dependent visual command recognition in vehicle cabin: methodology and evaluation. In: Karpov, A., Potapova, R. (eds.) SPECOM 2021. LNCS (LNAI), vol. 12997, pp. 291–302. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87802-3_27
Ivanko, D., Ryumin, D., Markitantov, M.: End-to-end visual speech recognition for human-robot interaction (2022)
Jung, S.G., An, J., Kwak, H., Salminen, J., Jansen, B.J.: Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race. In: Twelfth International AAAI Conference on Web and Social Media (2018)
Kim, J., El-Khamy, M., Lee, J.: Residual lstm: design of a deep recurrent architecture for distant speech recognition. arXiv preprint arXiv:1701.03360 (2017)
Kumar, J.R., Sujatha, B., Leelavathi, N.: Automatic vehicle number plate recognition system using machine learning. In: IOP Conference Series: Materials Science and Engineering, vol. 1074, p. 012012. IOP Publishing (2021)
Li, M., Huang, B., Tian, G.: A comprehensive survey on 3D face recognition methods. Eng. Appl. Artif. Intell. 110, 104669 (2022)
Li, Y., Guo, K., Lu, Y., Liu, L.: Cropping and attention based approach for masked face recognition. Appl. Intell. 51(5), 3012–3025 (2021). https://doi.org/10.1007/s10489-020-02100-9
Lin, S.W., Liu, Y.C.: The effects of motivations, trust, and privacy concern in social networking. Serv. Bus. 6(4), 411–424 (2012)
Liu, T., Das, R.K., Lee, K.A., Li, H.: Neural acoustic-phonetic approach for speaker verification with phonetic attention mask. IEEE Signal Process. Lett. 29, 782–786 (2022)
Liu, Z., Luo, P., Wang, X., Tang, X.: Large-scale celebfaces attributes (celeba) dataset. Retr. August 15(2018), 11 (2018)
Patel, C., Shah, D., Patel, A.: Automatic number plate recognition system (anpr): a survey. Int. J. Comput. Appl. 69(9), 21–33 (2013)
Puranic, A., Deepak, K., Umadevi, V.: Vehicle number plate recognition system: a literature review and implementation using template matching. Int. J. Comput. Appl. 134(1), 12–16 (2016)
Raharja, N.M., Fathansyah, M.A., Chamim, A.N.N.: Vehicle parking security system with face recognition detection based on eigenface algorithm. J. Rob. Control (JRC) 3(1), 78–85 (2022)
Rajasekar, V., et al.: Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm. Sci. Rep. 12(1), 1–11 (2022)
Rosenberg, A.E.: Automatic speaker verification: a review. Proc. IEEE 64(4), 475–487 (1976)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Setiyono, B., Amini, D.A., Sulistyaningrum, D.R.: Number plate recognition on vehicle using yolo-darknet. In: Journal of Physics: Conference Series, vol. 1821, p. 012049. IOP Publishing (2021)
Smith, R.: An overview of the tesseract ocr engine. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, pp. 629–633. IEEE (2007)
Tong, F., et al.: Asv-subtools: open source toolkit for automatic speaker verification. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6184–6188. IEEE (2021)
Verma, A., Goyal, A., Kumar, N., Tekchandani, H.: Face recognition: a review and analysis. In: Computational Intelligence in Data Mining, pp. 195–210 (2022)
Wu, H., Li, X., Liu, A.T., Wu, Z., Meng, H., Lee, H.y.: Adversarial defense for automatic speaker verification by cascaded self-supervised learning models. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6718–6722. IEEE (2021)
Wu, H., Li, X., Liu, A.T., Wu, Z., Meng, H., Lee, H.Y.: Improving the adversarial robustness for speaker verification by self-supervised learning. IEEE/ACM Trans. Audio Speech Lang. Process. 30, 202–217 (2021)
Xia, H., Xu, S., Liu, Y., Wei, X., Jia, H.: Research on the construction of intelligent vehicle verification system for road transportation. In: Jain, L.C., Kountchev, R., Hu, B., Kountcheva, R. (eds.) Smart Communications, Intelligent Algorithms and Interactive Methods. SIST, vol. 257, pp. 97–103. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-5164-9_12
Xiang, J., Zhu, G.: Joint face detection and facial expression recognition with mtcnn. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 424–427. IEEE (2017)
Zen, H., et al.: Libritts: a corpus derived from librispeech for text-to-speech. arXiv preprint arXiv:1904.02882 (2019)
Zhang, Q., Zhuo, L., Zhang, S., Li, J., Zhang, H., Li, X.: Fine-grained vehicle recognition using lightweight convolutional neural network with combined learning strategy. In: 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), pp. 1–5. IEEE (2018)
Zhou, T., Zhao, Y., Wu, J.: Resnext and res2net structures for speaker verification. In: 2021 IEEE Spoken Language Technology Workshop (SLT), pp. 301–307. IEEE (2021)
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This research is funded and supported by the ‘Research and Extension’ section of Rajshahi University of Engineering & Technology, Rajshahi-6204, Bangladesh.
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Sayeed, A., Srizon, A.Y., Hasan, M.M., Shin, J., Hasan, M.A.M., Mahmud, M.R. (2023). A Hybrid Campus Security System Combined of Face, Number-Plate, and Voice Recognition. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_27
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