Enhancing Face Recognition Accuracy On Low-Resolution Databases Using Interpolation Techniques And Feature Extraction Techniques
Main Article Content
Abstract
The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images. Image super-resolution is one of the principal techniques for generating high-resolution images. This research investigates the impact of image resolution on the performance of face recognition systems and proposes methods to enhance recognition accuracy on low-resolution face databases. In the first phase, several holistic face recognition algorithms, including Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and RESNET50, are evaluated for their performance on low-resolution face images. Subsequently, three interpolation techniques - nearest neighbor, bilinear, and bicubic interpolant - are applied as preprocessing steps to increase the resolution of the input images. The study aims to determine the effectiveness of these techniques in improving recognition accuracy. Various evaluation metrics, including accuracy, precision, sensitivity, specificity, Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM), are employed to assess the performance of the recognition systems. The results demonstrate the efficacy of the proposed approach in enhancing recognition accuracy on low-resolution face datasets, thereby contributing to the advancement of face recognition technology in practical applications.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
T. Huang, Z.Xiong, and Z. Zhang. "Face recognition applications." In Handbook of Face Recognition, Springer London, (2011):617-638
Y. M. Lui, D. Bolme, B. A. Draper, J. R. Beveridge, G. Givens, and P.J. Phillips, “A meta-analysis of face recognition covariates,” in Proc.Int. Conf. Biometrics: Theory, Appl. Syst., (2009): 1–8.
Boom, B. J., G. M. Beumer, L. J. Spreeuwers, and R.N.Veldhuis. "The effect of image resolution on the performance of a face recognition system." In Control, Automation, Robotics and Vision, 2006. ICARCV'06. 9th International Conference on, IEEE,(2006):1-6.
J. Wang, C. Zhang, and H.Y. Shum, “Face image resolution versus face recognition preformance based on two global methods,” Proceedings of Asia Conference on Computer Vision, 2004.
Kappeler, A., Yoo, S., Dai, Q., Katsaggelos, A.K.: Video super-resolution with convolutional neural networks. IEEE Transactions on Computational Imaging (2016)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zhao, Y.; Huang, Q. Image Enhancement of Robot Welding Seam Based on Wavelet Transform and Contrast Guidance. Int. J. Innov. Comput. Inf. Control 2022, 18, 149–159.
Lama, R.K.; Shin, S.; Kang, M.; Kwon, G.R.; Choi, M.R. Interpolation Using Wavelet Transform and Discrete Cosine Transform for High Resolution Display. In Proceedings of the 2016 IEEE International Conference on Consumer Electronics, ICCE 2016, Las Vegas, NV, USA, 7–11 January 2016.
S. Biswas, K. W. Bowyer, and P. J. Flynn. "Multidimensional scaling for matching low-resolution face images." Pattern Analysis and Machine Intelligence, IEEE Transactions on 34, no. 10 (2012): 2019-2030.
A. Chakrabarti, A. N. Rajagopalan, and R. Chellappa, “Super-resolutionof face images using kernel PCA-based prior,” IEEE Trans. Multimedia,vol. 9, no. 4, (Jun. 2007): 888–892
Jia, Z.; Huang, Q. Image Interpolation with Regional Gradient Estimation. Appl. Sci. 2022, 12, 7359.
Pratt, W.K. Digital Image Processing, 4th Edition. J. Electron. Imaging 2007, 16, 029901.
Daniel Schulz; Jose Maureira; Juan Tapia; Christoph Busch Identity Documents Image Quality Assessment 2022 30th European Signal Processing Conference (EUSIPCO) ,2022
Ang Li Jian Hu; Chilin Fu;Xiaolu Zhang ;Jun Zhou Attribute-Conditioned Face Swapping Network for Low-Resolution Images ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Year: 2022
Qiye Lian; Xiaohua Xie; Huicheng Zheng; Yongdong Zhang Variance of Local Contribution: an Unsupervised Image Quality Assessment for Face Recognition 2022 26th International Conference on Pattern Recognition (ICPR) ,2022
Sebastián González; Juan TapiaTowards Refining ID Cards Presentation Attack Detection Systems using Face Quality Index 2022 30th European Signal Processing Conference (EUSIPCO) 2022
Weisong Zhao; Xiangyu Zhu; Haichao Shi;Xiao-Yu Zhang;Zhen Lei Consistent Sub-Decision Network for Low-Quality Masked Face Recognition IEEE Signal Processing Letters 2022 ,Volume: 29 ,Journal Article
Biying Fu;Naser Damer Explain ability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) 2022
Qiyu Wei; Xulei Yang; Tong Sang; Huijiao Wang; Zou Xiaofeng; Cheng Zhongyao; Zhao Ziyuan; Zeng Zeng Latent Vector Prototypes Guided Conditional Face Synthesis 2022 IEEE International Conference on Image Processing (ICIP) 2022
Xiaoguang Tu; Jian Zhao; Qiankun Liu; Wenjie Ai; Guodong Guo; Zhifeng Li; Wei Liu; Jiashi Feng Joint Face Image Restoration and Frontalization for RecognitionIEEE Transactions on Circuits and Systems for Video Technology ,2022 | Volume: 32, Issue: 3
Ying Tai Feida Zhu; Junwei Zhu; Wenqing Chu; Xinyi Zhang; Xiaozhong Ji; Chengjie Wang; Blind Face Restoration via Integrating Face Shape and Generative Priors 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),2022
M.Benedict Tephila; B. Maruthi Shankar; S. Abhinandhan; K.K. Arjun; P.M. Aswini; C.Divya Priya An Experimental Approach in Colour Correction and Contrast Improvement for Underwater Images 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) ,2022
Wang, Z.; Li, L.; Xue, Y.; Jiang, C.; Wang, J.; Sun, K.; Ma, H. FeNet: Feature Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–12
Lijing Bu, Dong Dai Zhengpeng Zhang , Yin Yang and Mingjun (2023)Hyperspectral Super Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation Deng Remote Sens. 2023, 15, 3225.
Bhavna Bhadkare, Dr. Varsha Jotwani (2024), Enhancing Low-Resolution Images: A Review of Deep Learning Approaches, IOSR Journal of Computer Engineering (IOSR-JCE); e-ISSN: 2278-0661, Volume 26, Issue 1, Ser. 1 (Jan. – Feb. 2024), pp 39-48. doi: 10.9790/0661-2601013948.