Application of the nearest neigbour interpolation method and naives bayes classifier for the identification of bespectacled faces
Article Sidebar
Published:
Jun 30, 2024
Keywords:
Classification, Face identification, Glasses, Interpolasi nearest neigbour, Naives bayes
Section
Articles
Statistics Article
Article View : 108 Times
Main Article Content
Abstract
Facial recognition technology has rapidly advanced, but identifying individuals wearing glasses remains challenging due to altered or obscured facial features. This study addresses this issue by combining the Nearest Neighbor Interpolation Method and Naive Bayes Classification for bespectacled face identification. The method applies interpolation to enhance facial image quality, preserving critical features before classification by Naive Bayes into spectacle and non-spectacle classes. Using the Kaggle MeGlass dataset for training and testing, the approach achieved a training accuracy of 78%, a testing accuracy of 76%, and a cross-validation value of 0.70. These results indicate a significant improvement in recognizing bespectacled faces, contributing to enhanced accuracy in facial recognition systems. Despite these advancements, further improvements are possible, such as integrating more advanced models and expanding the dataset, which could lead to even greater accuracy and reliability in practical applications. This research provides a novel solution to a persistent challenge in facial recognition technology
Downloads
Download data is not yet available.
Article Details
How to Cite
Murtopo, A. A. ., Januarto, S. ., Anandianskha, S. ., & Gunawan, G. . (2024). Application of the nearest neigbour interpolation method and naives bayes classifier for the identification of bespectacled faces. Journal of Intelligent Decision Support System (IDSS), 7(2), 155-162. https://doi.org/10.35335/idss.v7i2.242
References
Ahmed, K., Gad, M. A., & Aboutabl, A. E. (2023). Snake species classification using deep learning techniques. Multimedia Tools and Applications, 1–42.
Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022). Structural crack detection using deep convolutional neural networks. Automation in Construction, 133, 103989.
Ali, W., Tian, W., Din, S. U., Iradukunda, D., & Khan, A. A. (2021a). Classical and modern face recognition approaches: a complete review. Multimedia Tools and Applications, 80, 4825–4880.
Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2), 115–128.
Bao, F., Wu, Y., Li, Z., Li, Y., Liu, L., & Chen, G. (2020). Effect improved for high-dimensional and unbalanced data anomaly detection model based on KNN-SMOTE-LSTM. Complexity, 2020, 1–17.
Cerqueira, V., Torgo, L., & Mozetič, I. (2020). Evaluating time series forecasting models: An empirical study on performance estimation methods. Machine Learning, 109(11), 1997–2028.
Dargan, S., & Kumar, M. (2020). A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143, 113114.
Ge, H., Bo, Y., Sun, H., Zheng, M., & Lu, Y. (2022). A review of research on driving distraction based on bibliometrics and co-occurrence: focus on driving distraction recognition methods. Journal of Safety Research, 82, 261–274. https://doi.org/10.1016/j.jsr.2022.06.002
Gunawardena, N., Ginige, J. A., & Javadi, B. (2022). Eye-tracking technologies in mobile devices Using edge computing: a systematic review. ACM Computing Surveys, 55(8), 1–33. https://doi.org/10.1145/3546938
Hedman, P., Skepetzis, V., Hernandez-Diaz, K., Bigun, J., & Alonso-Fernandez, F. (2022). On the effect of selfie beautification filters on face detection and recognition. Pattern Recognition Letters, 163, 104–111.
Jayaraman, U., Gupta, P., Gupta, S., Arora, G., & Tiwari, K. (2020). Recent development in face recognition. Neurocomputing, 408, 231–245.
Karnati, M., Seal, A., Bhattacharjee, D., Yazidi, A., & Krejcar, O. (2023). Understanding deep learning techniques for recognition of human emotions using facial expressions: A comprehensive survey. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2023.3243661
Kostka, G., Steinacker, L., & Meckel, M. (2021). Between security and convenience: Facial recognition technology in the eyes of citizens in China, Germany, the United Kingdom, and the United States. Public Understanding of Science, 30(6), 671–690.
Luan, X., Zheng, J., & Li, W. (2021). Learning unsupervised face normalization through frontal view reconstruction. IEEE Transactions on Circuits and Systems for Video Technology, 32(8), 5201–5212. https://doi.org/10.1109/TCSVT.2021.3136589
Malgheet, J. R., Manshor, N. B., Affendey, L. S., & Abdul Halin, A. Bin. (2021). Iris recognition development techniques: a comprehensive review. Complexity, 2021, 1–32.
Nguyen, T. D., Rieger, P., De Viti, R., Chen, H., Brandenburg, B. B., Yalame, H., Möllering, H., Fereidooni, H., Marchal, S., & Miettinen, M. (2022). {FLAME}: Taming backdoors in federated learning. 31st USENIX Security Symposium (USENIX Security 22), 1415–1432.
Oloyede, M. O., Hancke, G. P., & Myburgh, H. C. (2020a). A review on face recognition systems: recent approaches and challenges. Multimedia Tools and Applications, 79, 27891–27922.
Rajput, S. S., & Arya, K. V. (2020). A robust face super-resolution algorithm and its application in low-resolution face recognition system. Multimedia Tools and Applications, 79(33), 23909–23934. https://doi.org/10.1007/s11042-020-09072-5
Sharma, A., Singh, P. K., & Chandra, R. (2022). SMOTified-GAN for class imbalanced pattern classification problems. Ieee Access, 10, 30655–30665.
Wang, M., & Deng, W. (2021). Deep face recognition: A survey. Neurocomputing, 429, 215–244.
Wang, Y., Huang, G., Song, S., Pan, X., Xia, Y., & Wu, C. (2021). Regularizing deep networks with semantic data augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3733–3748. https://doi.org/10.1109/TPAMI.2021.3052951
Xiong, Z., Cui, Y., Liu, Z., Zhao, Y., Hu, M., & Hu, J. (2020). Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Computational Materials Science, 171, 109203.
Yaacoub, J.-P. A., Noura, M., Noura, H. N., Salman, O., Yaacoub, E., Couturier, R., & Chehab, A. (2020). Securing internet of medical things systems: Limitations, issues and recommendations. Future Generation Computer Systems, 105, 581–606. https://doi.org/10.1016/j.future.2019.12.028
Zhong, Y., Oh, S., & Moon, H. C. (2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technology in Society, 64, 101515. https://doi.org/10.1016/j.techsoc.2020.101515
Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022). Structural crack detection using deep convolutional neural networks. Automation in Construction, 133, 103989.
Ali, W., Tian, W., Din, S. U., Iradukunda, D., & Khan, A. A. (2021a). Classical and modern face recognition approaches: a complete review. Multimedia Tools and Applications, 80, 4825–4880.
Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2), 115–128.
Bao, F., Wu, Y., Li, Z., Li, Y., Liu, L., & Chen, G. (2020). Effect improved for high-dimensional and unbalanced data anomaly detection model based on KNN-SMOTE-LSTM. Complexity, 2020, 1–17.
Cerqueira, V., Torgo, L., & Mozetič, I. (2020). Evaluating time series forecasting models: An empirical study on performance estimation methods. Machine Learning, 109(11), 1997–2028.
Dargan, S., & Kumar, M. (2020). A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143, 113114.
Ge, H., Bo, Y., Sun, H., Zheng, M., & Lu, Y. (2022). A review of research on driving distraction based on bibliometrics and co-occurrence: focus on driving distraction recognition methods. Journal of Safety Research, 82, 261–274. https://doi.org/10.1016/j.jsr.2022.06.002
Gunawardena, N., Ginige, J. A., & Javadi, B. (2022). Eye-tracking technologies in mobile devices Using edge computing: a systematic review. ACM Computing Surveys, 55(8), 1–33. https://doi.org/10.1145/3546938
Hedman, P., Skepetzis, V., Hernandez-Diaz, K., Bigun, J., & Alonso-Fernandez, F. (2022). On the effect of selfie beautification filters on face detection and recognition. Pattern Recognition Letters, 163, 104–111.
Jayaraman, U., Gupta, P., Gupta, S., Arora, G., & Tiwari, K. (2020). Recent development in face recognition. Neurocomputing, 408, 231–245.
Karnati, M., Seal, A., Bhattacharjee, D., Yazidi, A., & Krejcar, O. (2023). Understanding deep learning techniques for recognition of human emotions using facial expressions: A comprehensive survey. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2023.3243661
Kostka, G., Steinacker, L., & Meckel, M. (2021). Between security and convenience: Facial recognition technology in the eyes of citizens in China, Germany, the United Kingdom, and the United States. Public Understanding of Science, 30(6), 671–690.
Luan, X., Zheng, J., & Li, W. (2021). Learning unsupervised face normalization through frontal view reconstruction. IEEE Transactions on Circuits and Systems for Video Technology, 32(8), 5201–5212. https://doi.org/10.1109/TCSVT.2021.3136589
Malgheet, J. R., Manshor, N. B., Affendey, L. S., & Abdul Halin, A. Bin. (2021). Iris recognition development techniques: a comprehensive review. Complexity, 2021, 1–32.
Nguyen, T. D., Rieger, P., De Viti, R., Chen, H., Brandenburg, B. B., Yalame, H., Möllering, H., Fereidooni, H., Marchal, S., & Miettinen, M. (2022). {FLAME}: Taming backdoors in federated learning. 31st USENIX Security Symposium (USENIX Security 22), 1415–1432.
Oloyede, M. O., Hancke, G. P., & Myburgh, H. C. (2020a). A review on face recognition systems: recent approaches and challenges. Multimedia Tools and Applications, 79, 27891–27922.
Rajput, S. S., & Arya, K. V. (2020). A robust face super-resolution algorithm and its application in low-resolution face recognition system. Multimedia Tools and Applications, 79(33), 23909–23934. https://doi.org/10.1007/s11042-020-09072-5
Sharma, A., Singh, P. K., & Chandra, R. (2022). SMOTified-GAN for class imbalanced pattern classification problems. Ieee Access, 10, 30655–30665.
Wang, M., & Deng, W. (2021). Deep face recognition: A survey. Neurocomputing, 429, 215–244.
Wang, Y., Huang, G., Song, S., Pan, X., Xia, Y., & Wu, C. (2021). Regularizing deep networks with semantic data augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3733–3748. https://doi.org/10.1109/TPAMI.2021.3052951
Xiong, Z., Cui, Y., Liu, Z., Zhao, Y., Hu, M., & Hu, J. (2020). Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Computational Materials Science, 171, 109203.
Yaacoub, J.-P. A., Noura, M., Noura, H. N., Salman, O., Yaacoub, E., Couturier, R., & Chehab, A. (2020). Securing internet of medical things systems: Limitations, issues and recommendations. Future Generation Computer Systems, 105, 581–606. https://doi.org/10.1016/j.future.2019.12.028
Zhong, Y., Oh, S., & Moon, H. C. (2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technology in Society, 64, 101515. https://doi.org/10.1016/j.techsoc.2020.101515
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.