A low-resolution real-time face recognition using extreme learning machine and its variants

References

• Madhavan S, Kumar N. Incremental methods in face recognition: a survey. Artif Intell Rev. 2021;54(1):253–303.
   Web of Science ®Google Scholar
• Ali W, Tian W, Din SU, et al. Classical and modern face recognition approaches: a complete review. Multimed Tools Appl. 2021;80(3):4825–4880.
   Web of Science ®Google Scholar
• Venkatesh S, Ramachandra R, Raja K, et al. Face morphing attack generation & detection: A comprehensive survey. IEEE Trans Technol Soc. 2021;2(3):128–145.
   Google Scholar
• Erkin Z, Franz M, Guajardo J, et al. Privacy-preserving face recognition. In: International Symposium on Privacy Enhancing Technologies Symposium. Springer; 2009. pp. 235–253.
   Google Scholar
• Pantic M, Nijholt A, Pentland A, et al. Human-centred intelligent human? computer interaction (hci2): how far are we from attaining it?. Int J Auton Adapt Commun Syst. 2008;1(2):168–187.
   Google Scholar
• De-la Torre M, Granger E, Radtke PV, et al. Partially-supervised learning from facial trajectories for face recognition in video surveillance. Inf Fusion. 2015;24:31–53.
   Web of Science ®Google Scholar
• Abbas A, Khalil MI, AbdelHay S, et al. Illumination invariant face recognition in logarithm discrete cosine transform domain. In: 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE; 2009. p. 4157–4160.
   Google Scholar
• Faraji MR, Qi X. Face recognition under varying illumination with logarithmic fractal analysis. IEEE Signal Process Lett. 2014;21(12):1457–1461.
   Web of Science ®Google Scholar
• Shekar B, Rajesh D, Kumari MS. Logarithmic dyadic wavelet transform based face recognition. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE; 2015. p. 1428–1435.
   Google Scholar
• Kang Y, Pan W. A novel approach of low-light image denoising for face recognition. Adv Mech Eng. 2014;6:Article ID 256790.
   Google Scholar
• Chude-Olisah CC, Sulong G, Chude-Okonkwo UA, et al. Illumination normalization for edge-based face recognition using the fusion of RGB normalization and gamma correction. In: 2013 IEEE International Conference on Signal and Image Processing Applications. IEEE; 2013. p. 412–416.
   Google Scholar
• Liu H-D, Yang M. Local histogram specification using learned histograms for face recognition. In: 2012 19th IEEE International Conference on Image Processing. IEEE; 2012. p. 597–600.
   Google Scholar
• Peddigari VR, Srinivasa P, Kumar R. Enhanced ICA based face recognition using histogram equalization and mirror image superposition. In: 2015 IEEE International Conference on Consumer Electronics (ICCE). IEEE; 2015. p. 625–628.
   Google Scholar
• Salhi AI, Kardouchi M, Belacel N. Histograms of fuzzy oriented gradients for face recognition. In: 2013 International Conference on Computer Applications Technology (ICCAT). IEEE; 2013. p. 1–5.
   Google Scholar
• Wang H, Zhang D, Miao Z. Fusion of LDB and HOG for face recognition. In: 2018 37th Chinese Control Conference (CCC). IEEE; 2018. p. 9192–9196.
   Google Scholar
• Koc M. A novel partition selection method for modular face recognition approaches on occlusion problem. Mach Vis Appl. 2021;32(1):1–11.
   Web of Science ®Google Scholar
• Lihong Z, Ying S, Yushi Z, et al. Face recognition based on multi-class SVM. In: 2009 Chinese Control and Decision Conference. IEEE; 2009. p. 5871–5873.
   Google Scholar
• Dong J, Gu J, Ma X, et al. Face recognition based on KPCA and SVM. In: Proceeding of the 11th World Congress on Intelligent Control and Automation. IEEE; 2014. p. 1439–1444.
   Google Scholar
• Wang T. A novel face recognition method based on ICA and binary tree SVM. In: 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). Vol. 1. IEEE; 2017. p. 251–254.
   Google Scholar
• Cui K, Han F, Wang P. Research on face recognition based on boolean kernel SVM. In: 2008 Fourth International Conference on Natural Computation. Vol. 2. IEEE; 2008. p. 148–152.
   Google Scholar
• Jianhong X. KPCA based on LS-SVM for face recognition. In: 2008 Second International Symposium on Intelligent Information Technology Application. Vol. 2. IEEE; 2008. p. 638–641.
   Google Scholar
• Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541). Vol. 2. IEEE; 2004. p. 985–990.
   Google Scholar
• Huang G-B, Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing. 2008;71(16–18):3460–3468.
   Web of Science ®Google Scholar
• Boussaad L, Boucetta A. Extreme learning machine-based age-invariant face recognition with deep convolutional descriptors. Int J Appl Metaheuristic Comput (IJAMC). 2022;13(1):1–18.
   Web of Science ®Google Scholar
• Sun C-J, Gao F. Remote sensing image recognition based on log-t-ssa-lssvm and ae-elm network. Comput Intell Neurosci. 2022;2022:1–12.
   Web of Science ®Google Scholar
• Yahia S, Said S, Zaied M. Wavelet extreme learning machine and deep learning for data classification. Neurocomputing. 2022;470:280–289.
   Web of Science ®Google Scholar
• Sreekala K, Cyril C, Neelakandan S, et al. Capsule network-based deep transfer learning model for face recognition. Wireless Communications and Mobile Computing. 2022;2022:1–12.
   Web of Science ®Google Scholar
• Li W, Li J, Zhou J. Deblurring method of face recognition ai technology based on deep learning. Advances in Multimedia. 2022;2022:1–9.
   Web of Science ®Google Scholar
• Hammouche R, Attia A, Akhrouf S, et al. Gabor filter bank with deep autoencoder based face recognition system. Expert Syst Appl. 2022;197(116743):1–12.
   Google Scholar
• Hussain T, Hussain D, Hussain I, et al. Internet of things with deep learning-based face recognition approach for authentication in control medical systems. Comput Math Methods Med. 2022;2022:1–17.
   Web of Science ®Google Scholar
• Park J, Kim K. Image perturbation-based deep learning for face recognition utilizing discrete cosine transform. Electronics. 2021;11(1):25.
   Web of Science ®Google Scholar
• Hattab A, Behloul A. New approaches for automatic face recognition based on deep learning models and local handcrafted altp. EAI Endorsed Trans Scalable Inf Syst. 2021;9(34):1–13.
   Google Scholar
• Wang Y, Li H, Guo Y. Face recognition based on ICA and SPSO-ELM. In: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE; 2017. p. 602–606.
   Google Scholar
• Zong W, Huang G-B. Face recognition based on extreme learning machine. Neurocomputing. 2011;74(16):2541–2551.
   Web of Science ®Google Scholar
• Sehra K, Rajpal A, Mishra A, et al. HOG based facial recognition approach using viola jones algorithm and extreme learning machine. In: International Conference on Computational Science and Its Applications. Springer; 2019. p. 423–435.
   Google Scholar
• Rajpal A, Mishra A, Sehra K. A novel os-elm classifier based on hog feature extraction and viola jones algorithm for facial recognition. Issues Inform Syst. 2019;20(2):117–127.
   Google Scholar
• Qu D, Huang Z, Gao Z, et al. An automatic system for smile recognition based on CNN and face detection. In: 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE; 2018. p. 243–247.
   Google Scholar
• He R, Wu X, Sun Z, et al. Wasserstein cnn: learning invariant features for nir-vis face recognition. IEEE Trans Pattern Anal Mach Intell. 2018;41(7):1761–1773.
   PubMed Web of Science ®Google Scholar
• Zhang H, Qu Z, Yuan L, et al. A face recognition method based on LBP feature for CNN. In: 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE; 2017. p. 544–547.
   Google Scholar
• Rajpal S, Agarwal M, Rajpal A, et al. Cov-elm classifier: an extreme learning machine based identification of covid-19 using chest x-ray images. Intell Dec Technol. 2022;Pre-press:1–11.
   Google Scholar
• Das NN, Kumar N, Kaur M, et al. Automated deep transfer learning-based approach for detection of covid-19 infection in chest x-rays. Irbm. 2020;43(2):114–119.
   Web of Science ®Google Scholar
• Shorfuzzaman M, Hossain MS. Metacovid: a siamese neural network framework with contrastive loss for n-shot diagnosis of covid-19 patients. Pattern Recognit. 2021;113:Article ID 107700.
   PubMed Web of Science ®Google Scholar
• Pathak Y, Shukla PK, Tiwari A, et al. Deep transfer learning based classification model for covid-19 disease. Irbm. 2020;43(2):87–92.
   Web of Science ®Google Scholar
• Shrivastava VK, Pradhan MK. Deep convolutional neural network based diagnosis of covid-19 using x-ray images. In Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 2, 2053–2563. IOP Publishing; 2020. p. 13–1 to 13–17.
   Google Scholar
• Rajpal S, Lakhyani N, Singh AK, et al. Using handpicked features in conjunction with resnet-50 for improved detection of covid-19 from chest x-ray images. Chaos Solitons Fract. 2021;145:Article ID 110749.
   PubMed Web of Science ®Google Scholar
• Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing. 2006;70(1-3):489–501.
   Web of Science ®Google Scholar
• Huang G-B, Chen L, Siew CK, et al. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw. 2006;17(4):879–892.
   PubMed Web of Science ®Google Scholar
• Huang G-B, Zhou H, Ding X, et al. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern). 2011;42(2):513–529.
   PubMed Web of Science ®Google Scholar
• Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cognit Comput. 2014;6(3):376–390.
   Web of Science ®Google Scholar
• Huang G-B. What are extreme learning machines? Filling the gap between Frank Rosenblatt's dream and John von Neumann's puzzle. Cognit Comput. 2015;7(3):263–278.
   Web of Science ®Google Scholar
• Li Y, Zhang S, Yin Y, et al. A novel online sequential extreme learning machine for gas utilization ratio prediction in blast furnaces. Sensors. 2017;17(8):1847.
   PubMed Web of Science ®Google Scholar
• Huang G-B, Liang N-Y, Rong H-J, et al. On-line sequential extreme learning machine. Comput Intell. 2005;2005:232–237.
   Google Scholar
• Chong EK, Zak SH. An introduction to optimization. 4th ed. New Jersey: John Wiley & Sons; 2013.
   Google Scholar
• Viola P, Jones MJ. Robust real-time face detection. Journal of Computer VisionInt J Comput Vis. 2004;57(2):137–154.
   Web of Science ®Google Scholar
• Fierro-Radilla A, Perez-Daniel K, Nakano-Miyatakea M, et al. An effective visual descriptor based on color and shape features for image retrieval. In: Mexican International Conference on Artificial Intelligence. Springer; 2014. p. 336–348.
   Google Scholar
• Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Vol. 1. IEEE; 2005. p. 886–893.
   Google Scholar
• McCormick C. Hog person detector tutorial. 2013.
   Google Scholar
• Norris MK. Incorporating histograms of oriented gradients into Monte Carlo localization [Master's thesis]. California Polytechnic State University; 2016.
   Google Scholar
• Matlab M. Extract histogram of oriented gradients (HOG) features. Available from: https://in.mathworks.com/help/vision/ref/extracthogfeatures.html?s_tid=doc_ta.
   Google Scholar
• Zong W, Zhou H, Huang G-B, et al. Face recognition based on kernelized extreme learning machine. In: International Conference on Autonomous and Intelligent Systems. Springer; 2011. p. 263–272.
   Google Scholar
• Zhang G-Y, Peng S-Y, Li H-M. Combination of dual-tree complex wavelet and SVM for face recognition. In: 2008 International Conference on Machine Learning and Cybernetics. Vol. 5. IEEE; 2008. p. 2815–2819.
   Google Scholar
• Gan J-Y, He S-B. Face recognition based on 2DLDA and support vector machine. In: 2009 International Conference on Wavelet Analysis and Pattern Recognition. IEEE; 2009. p. 211–214.
   Google Scholar
• Matlab M. Detect objects using the Viola-Jones algorithm. Available from: https://in.mathworks.com/help/vision/ref/vision.cascadeobjectdetector-system-object.html.
   Google Scholar
• Datal N. Histograms of oriented gradients for human detection. In: Proc. 2005 International Conference on Computer Vision and Pattern Recognition. Vol. 2. IEEE Computer Society; 2005. p. 886–893.
   Google Scholar
• Ge Y, Liu Y, Wang F, et al. Texture feature extraction based on histogram of oriented gradient domain texture tendency for tyre pattern retrieval. In: 2017 Eighth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE; 2017. p. 1–7.
   Google Scholar
• Chutani S, Goyal A. Improved universal quantitative steganalysis in spatial domain using elm ensemble. Multimed Tools Appl. 2018;77(6):7447–7468.
   Web of Science ®Google Scholar
• Mitchell T. CMU face images data set. Available from: http://archive.ics.uci.edu/ml/datasets/cmu+face+images.
   Google Scholar
• Jesorsky O, Kirchberg KJ, Frischholz RW. Robust face detection using the hausdorff distance. In: International Conference on Audio-and Video-Based Biometric Person Authentication. Springer; 2001. p. 90–95.
   Google Scholar
• Belhumeur PN, Hespanha JP, Kriegman DJ. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell. 1997;19(7):711–720.
   Web of Science ®Google Scholar
• Huang GB, Ramesh M, Berg T, et al. Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Amherst: University of Massachusetts; Oct 2007. (Tech. Rep. 07–49).
   Google Scholar
• Rajpal A, Mishra A, Bala R. Robust blind watermarking technique for color images using Online Sequential Extreme Learning Machine. In: 2015 International Conference on Computing, Communication and Security (ICCCS). IEEE; 2015. p. 1–7.
   Google Scholar
• Rajpal A, Mishra A, Bala R. Multiple scaling factors based Semi-Blind watermarking of grayscale images using OS-ELM neural network. In: 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE; 2016. p. 1–6.
   Google Scholar
• Python.org. Lfw Face Recognition using SVM. Available from: https://scikit-learn.org/0.15/_downloads/face_recognition.py.
   Google Scholar
• Ahadit AB, Jatoth RK. A novel multi-feature fusion deep neural network using hog and vgg-face for facial expression classification. Mach Vis Appl. 2022;33(4):1–23.
   Web of Science ®Google Scholar
• Mehdipour Ghazi M, Kemal Ekenel H. A comprehensive analysis of deep learning based representation for face recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA. 2016. p. 34–41.
   Google Scholar
• Rakshit RD, Kisku DR, Gupta P, et al. Cross-resolution face identification using deep-convolutional neural network. Multimed Tools Appl. 2021;80(14):20733–20758.
   Web of Science ®Google Scholar
• Limonova E, Sheshkus A, Ivanova A, et al. Convolutional neural network structure transformations for complexity reduction and speed improvement. Pattern Recognit Image Anal. 2018;28(1):24–33.
   Google Scholar
• Liang N-Y, Huang G-B, Saratchandran P, et al. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw. 2006;17(6):1411–1423.
   PubMed Web of Science ®Google Scholar
• Kale GA, Karakuzu C. Multilayer extreme learning machines and their modeling performance on dynamical systems. Appl Soft Comput. 2022;122:Article ID 108861.
   Web of Science ®Google Scholar