Effectiveness of Feature Extraction Techniques for Facial Identification
DOI:
https://doi.org/10.22399/ijcesen.822Keywords:
Principal component analysis, Local binary pattern, Convolutional neural network, Facial identification, Feature extractionAbstract
Criminal activities and crime tenancy are increasing in the society when the technology and population increases. The process of identifying and determine criminals and avoiding them from involving in criminal activities are tedious task for police as well as public. Therefore, criminal tracking system is also needed to strengthen. Apart from traditional system, now a days the police and government is also implementing technology based tracking system for criminal identification. An efficient facial feature extraction algorithm and face identification algorithm are needed for this identification system. In this research, the performance of principal component analysis and local binary pattern algorithms are analysed with the support of convolutional neural network.
References
Rey, W., Blancaflor, E., & Rey, K. W. J. D. (2022). Adoptability of open-source face recognition (FR) on automated criminal identification system for law enforcement in the Philippines: A systematic review. 12th International Conference on Software Technology and Engineering (ICSTE), SRM Institute of Science and Technology-RamaPuram. 91–95. DOI: 10.1109/ICSTE57415.2022.00020.
Jain, A. K., & Li, S. Z. (2011). Handbook of face recognition. Springer London. https://doi.org/10.1007/978-0-85729-932-1.
Dewi Agushinta, R., & Septadepi, I. (n.d.). Face recognition system using eigenface method based on facial component region.
Kanan, C., & Cottrell, G. W. (2012). Color-to-grayscale: Does the method matter in image recognition. PLoS ONE. 7(1), e29740. DOI: 10.1371/journal.pone.0029740.
Patil, P. R., & Kulkarni, S. S. (2021). Survey of non-intrusive face spoof detection methods. Multimedia Tools and Applications. 80(10);14693–14721. https://doi.org/10.1007/s11042-020-10338-1.
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.
Shetty, A. B., et al. (2021). Facial recognition using Haar cascade and LBP classifiers. Global Transitions Proceedings. 2(2);330–335. https://doi.org/10.1016/j.gltp.2021.08.044.
Nyoka, A. F., et al. (2023). Reliable face identification system for criminal investigation. 2023 11th International Symposium on Digital Forensics and Security (ISDFS), SRM Institute of Science and Technology-RamaPuram. DOI: 10.1109/ISDFS58141.2023.10131738.
Gosavi, V. R., Sable, G. S., & Deshmane, A. K. (2018). An efficient feature level extractions for facial recognition: A comprehensive review. 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE). 409–412. DOI: 10.1109/ICACCE.2018.8441749.
Shilaskar, S. (2023). Robust criminal identification system for recognition of obscure and hidden faces. 2nd International Conference on Futuristic Technologies (INCOFT), SRM Institute of Science and Technology-RamaPuram. DOI: 10.1109/INCOFT60753.2023.10425451.
Singh, G., & Goel, A. K. (2020). Face detection and recognition system using digital image processing. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). 348–352. DOI: 10.1109/ICIMIA48430.2020.9074838.
Bansal, A., Mehta, K., & Arora, S. (2012). Face recognition using PCA and LDA algorithm. 2012 Second International Conference on Advanced Computing & Communication Technologies, Rohtak, India. 251–254. DOI: 10.1109/ACCT.2012.52
Agarwal, M., Agrawal, H., Jain, N., & Kumar, M. (2010). Face recognition using principle component analysis, eigenface and neural network. 2010 International Conference on Signal Acquisition and Processing, Bangalore, India. 310–314. DOI: 10.1109/ICSAP.2010.51
Manna, S., Ghildiyal, S., & Bhimani, K. (2020). Face recognition from video using deep learning. 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India. 1101–1106. DOI: 10.1109/ICCES48766.2020.9137927
Bhatlawande, S., Shilaskar, S., Gadad, T., Ghulaxe, S., & Gaikwad, R. (2023). Smart home security monitoring system based on face recognition and Android application. 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India. 222–227. DOI: 10.1109/IDCIoT56793.2023.10053558
Ali, L. R., Homood, H. K., & Elameer, A. S. (2017). Feature extraction techniques on facial images: An overview. International Journal of Science and Research. 6(9);893–896. DOI: 10.21275/ART20176682.
Abiyev, R. H. (2014). Facial feature extraction techniques for face recognition. Journal of Computer Science. 10(12);2360–2365. https://doi.org/10.3844/jcssp.2014.2360.2365.
Heusch, G., Rodriguez, Y., & Marcel, S. (2005). Local binary patterns as an image preprocessing for face authentication. 7th International Conference on Automatic Face and Gesture Recognition (FGR06). 9–14. https://doi.org/10.1109/fgr.2006.72.
Lin, S. D., & Chen, K. (2019). Illumination invariant thermal face recognition using convolutional neural network. IEEE International Conference on Consumer Electronics – Asia (ICCE-Asia), Bangkok. 83–84. https://doi.org/10.1109/icce-asia46551.2019.8941593.
Syam Kumar Duggirala, M. Sathya, & Nithya Poupathy. (2025). Enhancing Secure Image Transmission Through Advanced Encryption Techniques. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.761
Johnsymol Joy, & Mercy Paul Selvan. (2025). An efficient hybrid Deep Learning-Machine Learning method for diagnosing neurodegenerative disorders. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.701
U. S. Pavitha, S. Nikhila, & Mohan, M. (2024). Hybrid Deep Learning Based Model for Removing Grid-Line Artifacts from Radiographical Images. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.514
Sreetha E S, G Naveen Sundar, & D Narmadha. (2024). Enhancing Food Image Classification with Particle Swarm Optimization on NutriFoodNet and Data Augmentation Parameters. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.493
Radhi, M., & Tahseen, I. (2024). An Enhancement for Wireless Body Area Network Using Adaptive Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3). https://doi.org/10.22399/ijcesen.409
Jha, K., Sumit Srivastava, & Aruna Jain. (2024). A Novel Texture based Approach for Facial Liveness Detection and Authentication using Deep Learning Classifier. International Journal of Computational and Experimental Science and Engineering, 10(3). https://doi.org/10.22399/ijcesen.369
Boddupally JANAIAH, & Suresh PABBOJU. (2024). HARGAN: Generative Adversarial Network BasedDeep Learning Framework for Efficient Recognition of Human Actions from Surveillance Videos. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.587
Sashi Kanth Betha. (2024). ResDenseNet:Hybrid Convolutional Neural Network Model for Advanced Classification of Diabetic Retinopathy(DR) in Retinal Image Analysis. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.693
T. Deepa, & Ch. D. V Subba Rao. (2025). Brain Glial Cell Tumor Classification through Ensemble Deep Learning with APCGAN Augmentation. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.803
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