Application of nearest neighbor interpolation method and naïve bayes classifier for identification of bespectacled faces
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Published:
Mar 30, 2024
Keywords:
Classifier, Face Recognition, Identification, Naïve Bayes, Nearest Neighbor
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Abstract
Facial identification has become necessary in the era of advanced technology, especially in security and human-computer interaction. However, accessories such as glasses often complicate the identification process. This research aims to develop a facial identification system that can recognize bespectacled individuals with high accuracy, overcoming the limitations of conventional facial recognition technology. The method combines nearest neighbor interpolation to improve image quality and Naïve Bayes classification to distinguish between bespectacled and non-spectacled faces. The results showed that the developed model effectively identified bespectacled subjects with a high recall rate, although accuracy and precision still needed improvement. The implications of this research are significant for the field of biometric security and facial recognition, offering new solutions for more inclusive and adaptive facial recognition systems and opening up opportunities for further research in method optimization and dataset quality improvement.
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Setiawan, D., Gunawan, G., & Zaenul Arif. (2024). Application of nearest neighbor interpolation method and naïve bayes classifier for identification of bespectacled faces. Journal of Intelligent Decision Support System (IDSS), 7(1), 85-92. https://doi.org/10.35335/idss.v7i1.213
References
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Valdiviezo-Diaz, P., Ortega, F., Cobos, E., & Lara-Cabrera, R. (2019). A collaborative filtering approach based on Naïve Bayes classifier. IEEE Access, 7, 108581–108592. https://doi.org/10.1109/ACCESS.2019.2933048
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Bahri, S., Samsinar, R., & Denta, P. S. (2022). Pengenalan Ekspresi Wajah untuk Identifikasi Psikologis Pengguna dengan Neural Network dan Transformasi Ten Crops. RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer), 5(1), 15. https://doi.org/10.24853/resistor.5.1.15-20
Blanquero, R., Carrizosa, E., Ramírez-Cobo, P., & Sillero-Denamiel, M. R. (2021). Variable selection for Naïve Bayes classification. Computers & Operations Research, 135, 105456. https://doi.org/10.1016/j.cor.2021.105456
Chen, L., Pan, J., Hu, R., Han, Z., Liang, C., & Wu, Y. (2019). Modeling and optimizing of the multi-layer nearest neighbor network for face image super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 30(12), 4513–4525. https://doi.org/10.1109/TCSVT.2019.2917511
Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21, 1–13. https://doi.org/10.1186/s12864-019-6413-7
Dewi, N., & Ismawan, F. (2021). Implementasi Deep Learning Menggunakan Cnn Untuk Sistem Pengenalan Wajah. Faktor Exacta, 14(1), 34. https://doi.org/10.30998/faktorexacta.v14i1.8989
Gu, J., & Lu, S. (2021). An effective intrusion detection approach using SVM with naïve Bayes feature embedding. Computers & Security, 103, 102158. https://doi.org/10.1016/j.cose.2020.102158
Jayaraman, U., Gupta, P., Gupta, S., Arora, G., & Tiwari, K. (2020). Recent development in face recognition. Neurocomputing, 408, 231–245. https://doi.org/10.1016/j.neucom.2019.08.110
Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 342. https://doi.org/10.3390/s20020342
Kumarahadi, Y. K., Arifin, M. Z., Pambudi, S., Prabowo, T., & Kusrini, K. (2020). Sistem Pakar Identifikasi Jenis Kulit Wajah Dengan Metode Certainty Factor. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 8(1), 21–27. https://doi.org/10.30646/tikomsin.v8i1.453
Li, L., Mu, X., Li, S., & Peng, H. (2020). A review of face recognition technology. IEEE Access, 8, 139110–139120. https://doi.org/10.1109/ACCESS.2020.3011028
Liantoni, F., & Nugroho, H. (2015). Klasifikasi Daun Herbal Menggunakan Metode Naïve Bayes Classifier Dan Knearest Neighbor. Jurnal Simantec, 5(1), 9–16.
Martínez-Díaz, Y., Méndez-Vázquez, H., Luevano, L. S., Nicolás-Díaz, M., Chang, L., & González-Mendoza, M. (2021). Towards accurate and lightweight masked face recognition: an experimental evaluation. IEEE Access, 10, 7341–7353. https://doi.org/10.1109/ACCESS.2021.3135255
Muthukrishnan, A., Kumar, D. V., & Kanagaraj, M. (2019). Internet of image things-discrete wavelet transform and Gabor wavelet transform based image enhancement resolution technique for IoT satellite applications. Cognitive Systems Research, 57, 46–53. https://doi.org/10.1016/j.cogsys.2018.10.010
Oloyede, M. O., Hancke, G. P., & Myburgh, H. C. (2020). A review on face recognition systems: recent approaches and challenges. Multimedia Tools and Applications, 79(37), 27891–27922. https://doi.org/10.1007/s11042-020-09261-2
Rathgeb, C., Dantcheva, A., & Busch, C. (2019). Impact and detection of facial beautification in face recognition: An overview. IEEE Access, 7, 152667–152678. https://doi.org/10.1109/ACCESS.2019.2948526
Sajjad, M., Nasir, M., Muhammad, K., Khan, S., Jan, Z., Sangaiah, A. K., Elhoseny, M., & Baik, S. W. (2020). Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Generation Computer Systems, 108, 995–1007. https://doi.org/10.1016/j.future.2017.11.013
Shi, Y., Zhang, Z., Huang, K., Ma, W., & Tu, S. (2020). Human-computer interaction based on face feature localization. Journal of Visual Communication and Image Representation, 70, 102740. https://doi.org/10.1016/j.jvcir.2019.102740
Soares, M. A. C., & Parreiras, F. S. (2020). A literature review on question answering techniques, paradigms and systems. Journal of King Saud University-Computer and Information Sciences, 32(6), 635–646. https://doi.org/10.1016/j.jksuci.2018.08.005
Sriyati, S., Setyanto, A., & Luthfi, E. E. (2020). Literature Review: Pengenalan Wajah Menggunakan Algoritma Convolutional Neural Network. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 8(2). https://doi.org/10.30646/tikomsin.v8i2.463
Valdiviezo-Diaz, P., Ortega, F., Cobos, E., & Lara-Cabrera, R. (2019). A collaborative filtering approach based on Naïve Bayes classifier. IEEE Access, 7, 108581–108592. https://doi.org/10.1109/ACCESS.2019.2933048
Zhang, W. K., & Kang, M. J. (2019). Factors affecting the use of facial-recognition payment: an example of Chinese consumers. Ieee Access, 7, 154360–154374. https://doi.org/10.1109/ACCESS.2019.2927705
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