Realtime Face Recognition System with Viola-Jones and Local Binary Pattern Histogram (LBPH) Method

Ahmad Ridwan; Rian Ferdian; Jumadi Mabe Parenreng; M. Udin Harun Al Rasyid

doi:10.69693/jesa.v1i2.12

Authors

Ahmad Ridwan Universitas Amikom Yogyakarta
Rian Ferdian Department of Computer Engineering, Faculty of Information Technology, Universitas Andalas, Padang, 25175, Indonesia
Jumadi Mabe Parenreng Department of Informatics and Computer Engineering, Universitas Negeri Makassar, Makassar, 90222, Indonesia
M. Udin Harun Al Rasyid Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia

DOI:

https://doi.org/10.69693/jesa.v1i2.12

Keywords:

Face Detection, Face Recognition, Viola-Jones, Local Binary Pattern Histogram, Realtime

Abstract

The face is one of the body parts that a detection system can detect. This is what underlies the existence of face detection. One technology that uses face identification is biometric identification. The Viola-Jones method determines whether an object is a face by extracting features in a face image and classifying it to decide whether or not it is a face. However, the Viola-Jones method has the disadvantage that it can only detect human faces. This research will combine the Viola-Jones method to recognize human faces with the Local Binary Pattern Histogram (LBPH) method. The result is that the system can detect and identify up to two human faces facing forward, sideways, up, and down for the database. An accuracy calculation is also added to measure the accuracy of face recognition after the database is retrieved and trained. This average percentage of correctness is taken from comparing the predicted face to be recognized with the face that will be recognized. The result will be compared again with the number of photos taken during the recognition process and multiplied by 100%.

Downloads

Download data is not yet available.

References

[1] A. Kumar, A. Kaur, and M. Kumar, “Face detection techniques: a review,” Artif. Intell. Rev., vol. 52, no. 2, pp. 927–948, 2019, doi: 10.1007/s10462-018-9650-2.

[2] Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face recognition systems: A survey,” Sensors (Switzerland), vol. 20, no. 2, 2020, doi: 10.3390/s20020342.

[3] A. N. Razzaq, R. Ghazali, N. K. El Abbadi, and M. Dosh, “A Comprehensive Survey on Face Detection Techniques,” Webology, vol. 19, no. 1, pp. 613–628, 2022, doi: 10.14704/web/v19i1/web19044.

[4] C. Wang and W. Deng, “Representative forgery mining for fake face detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021, pp. 14923–14932. doi: 10.1109/CVPR46437.2021.01468.

[5] D. Zeng, R. Veldhuis, and L. Spreeuwers, “A survey of face recognition techniques under occlusion,” IET Biometrics, vol. 10, no. 6, pp. 581–606, 2021, doi: 10.1049/bme2.12029.

[6] I. Adjabi, A. Ouahabi, A. Benzaoui, and A. Taleb-Ahmed, “Past, present, and future of face recognition: A review,” Electron., vol. 9, no. 8, pp. 1–53, 2020, doi: 10.3390/electronics9081188.

[7] S. V. N. V. S. Sudeep, S. Venkata Kiran, D. Nandan, and S. Kumar, “An Overview of Biometrics and Face Spoofing Detection BT - ICCCE 2020,” in ICCCE 2020: Proceedings of the 3rd International Conference on Communications and Cyber Physical Engineering, A. Kumar and S. Mozar, Eds., Singapore: Springer Nature Singapore, 2021, pp. 871–881.

[8] A. Khuran, B. P. Lohani, V. Bibhu, and P. K. Kushwaha, “An AI Integrated Face Detection System for Biometric Attendance Management,” in Proceedings of 2021 2nd International Conference on Intelligent Engineering and Management, ICIEM 2021, 2021, pp. 29–33. doi: 10.1109/ICIEM51511.2021.9445295.

[9] H. F. Menezes, A. S. C. Ferreira, E. T. Pereira, and H. M. Gomes, “Bias and Fairness in Face Detection,” in 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2021, pp. 247–254. doi: 10.1109/SIBGRAPI54419.2021.00041.

[10] M. Khan, S. Chakraborty, R. Astya, and S. Khepra, “Face Detection and Recognition Using OpenCV,” in 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2019, pp. 116–119. doi: 10.30880/jscdm.2021.02.02.008.

[11] H. Adusumalli, D. Kalyani, R. K. Sri, M. Pratapteja, and P. V. R. D. P. Rao, “Face Mask Detection Using OpenCV,” in 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 2021, pp. 1304–1309. doi: 10.4028/p-2ffx83.

[12] R. T. H. Hasan and A. B. Sallow, “Face Detection and Recognition Using OpenCV,” J. Soft Comput. Data Min., vol. 2, no. 2, pp. 86–97, 2021, doi: 10.30880/jscdm.2021.02.02.008.

[13] C. Bisogni, A. Castiglione, F. Narducci, and C. Pero, “Fractal encoding uses and variations in contemporary biometrics applications,” in 2023 24th International Conference on Control Systems and Computer Science (CSCS), 2023, pp. 281–287. doi: 10.1109/CSCS59211.2023.00051.

[14] B. Meden et al., “Privacy-Enhancing Face Biometrics: A Comprehensive Survey,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 4147–4183, 2021, doi: 10.1109/TIFS.2021.3096024.

[15] G. Singh and A. K. Goel, “Face Detection and Recognition System using Digital Image Processing,” in 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020 - Conference Proceedings, 2020, pp. 348–352. doi: 10.1109/ICIMIA48430.2020.9074838.

[16] M. S. S. K. Varma, S. Abhishek, S. Dandu, and T. A., “Human Face Detection and Recognition using Artificial Intelligence,” in 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), 2023, pp. 769–775. doi: 10.1109/ICOEI56765.2023.10126074.

[17] C. H. Choi, J. Kim, J. Hyun, Y. Kim, and B. Moon, “Face Detection Using Haar Cascade Classifiers Based on Vertical Component Calibration,” Human-centric Comput. Inf. Sci., vol. 12, 2022, doi: 10.22967/HCIS.2022.12.011.

[18] F. M. Javed Mehedi Shamrat, A. Majumder, P. R. Antu, S. K. Barmon, I. Nowrin, and R. Ranjan, “Human Face Recognition Applying Haar Cascade Classifier,” in Pervasive Computing and Social Networking, G. Ranganathan, R. Bestak, R. Palanisamy, and Á. Rocha, Eds., Singapore: Springer Nature Singapore, 2022, pp. 143–157.

[19] A. B. Shetty, Bhoomika, Deeksha, J. Rebeiro, and Ramyashree, “Facial recognition using Haar cascade and LBP classifiers,” Glob. Transitions Proc., vol. 2, no. 2, pp. 330–335, 2021, doi: 10.1016/j.gltp.2021.08.044.

[20] F. S. Riyadi, G. Gunawan, and Z. Arif, “Application of computer vision for face recognition using viola jones algorithm method,” J. Intell. Decis. Support Syst., vol. 7, no. 1, pp. 27–34, 2024, doi: 10.35335/idss.v7i1.204.

[21] N. Bakshi and V. Prabhu, “Face recognition system for access control using principal component analysis,” in ICCT 2017 - International Conference on Intelligent Communication and Computational Techniques, 2017, pp. 145–150. doi: 10.1109/INTELCCT.2017.8324035.

[22] A. Muliani and S. Kosasih, “PERANCANGAN APLIKASI PENGENALAN CITRA WAJAH MENGGUNAKAN METODE COMPLETE KERNEL FISHER DISCRIMINANT,” J. Teknol. Inf., vol. 3, no. 1, pp. 92–99, 2019, doi: 10.36294/jurti.v3i1.702.

[23] M. Athoillah, “Pengenalan Wajah Menggunakan SVM Multi Kernel dengan Pembelajaran yang Bertambah,” J. Online Inform., vol. 2, no. 2, p. 84, 2018, doi: 10.15575/join.v2i2.109.

[24] A. R. Syafira, “Sistem Deteksi Wajah Dengan Modifikasi Metode Viola Jones,” J. Tek. Elektro, vol. 17, no. 01, pp. 1–8, 2020.

[25] F. L. Ramadini and E. Haryatmi, “Penggunaan Metode Haar Cascade Classifier dan LBPH Untuk Pengenalan Wajah Secara Realtime,” InfoTekJar J. Nas. Inform. dan Teknol. Jar., vol. 6, no. 2, pp. 1–8, 2022, [Online]. Available: https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/4714/pdf