Abstract
A biometric method for identifying people is face recognition. In the face recognition process, the key step is to extract the distinctive features of each person’s image. One of the most widely used tools for this purpose is the Gabor filter bank. A Gabor filter bank can extract powerful distinguishing features from a face image, but the disadvantage is that it imposes a high computational complexity on the face recognition system. The present paper introduces two new Gabor filter banks, i.e., the Optimal Gabor Filter Bank (OGFB) and the Personal Gabor Filter Bank (PGFB), which can reduce the computational complexity of a face recognition system by more than 7.5 and 30 times, respectively. It also introduces a new feature called Square Region of Face (SRoF) which is as easy to implement as global features, while taking into account the geometric position of facial features, including eyes, nose, and lips. This new feature is resistant to changes of hairstyle, eyebrows shape, and their color, as well as to the covered part of faces especially by different types of Islamic veils. Experiments on benchmark datasets of Caltech, Yale, Feret, and CsetM show that the proposed methods achieve better or competitive classification accuracy compared to several recent face recognition systems.
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Abderazek H, Yildiz AR, Mirjalili S (2019) Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism. Knowl-Based Syst 191:52
Aggarwal CC (2018) Neural networks and deep learning: a textbook. Springer
Agrawal S, Panda R, Kumari S, Dora L, Abraham A (2019) A new hybrid multifocus image fusion model using single optimum Gabor filter. Rev d’Intelligence Artif 33:111–118
Alagarsamy SB, Murugan K (2021) Multimodal of ear and face biometric recognition using adaptive approach runge–kutta threshold segmentation and classifier with score level fusion. Wirel Pers Commun
Ali KS, Ishtiaqa M, Nazir M, Shaheen M (2018) Face recognition under varying expressions and illumination using particle swarm optimization. J Comput Sci 28:94–100
Alphonse AS, Dharma D (2017) Enhanced Gabor (E-Gabor), hypersphere-based normalization and Pearson general kernel-based discriminant analysis for dimension reduction and classification of facial emotions. Expert Syst Appl 90:127–145
Bartholomew DJ (2010) Principal components analysis. In: Peterson P (ed) International encyclopedia of education, 3rd edn. Elsevier Science, pp 374–377
Bastanfard A, Takahashi H, Nakajima M (2004) Toward E-appearance of human face and hair by age, expression and rejuvenation. In: International conference on Cyberworlds, pp 306–311
Bastanfard A, Nakajima M, Takahashi H, Bastanfard O (2004) Toward anthropometrics simulation of face rejuvenation and skin cosmetic: research articles. Comput Animat Virtual Worlds 15:347–352
Beli ILK, Guo C (2017) Enhancing face identification using local binary patterns and K-nearest neighbors. J Imaging 3:1–12
Biswas S, Sil J (2020) An efficient face recognition method using contourlet and curvelet transform. J King Saud Univ Comput Inf Sci 32:718–729
Cament LA, Galdames FJ, Bowyer KW, Perez CA (2015) Face recognition under pose variation with local Gabor features enhanced by active shape and statistical models. Pattern Recogn 48:3371–3384
Chahla C, Snoussi H, Abdallah F, Dornaika F (2020) Learned versus handcrafted features for person re-identification. Int J Pattern Recognit Artif Intell 34:1–19
Chakraborti T, McCane B, Mills S, Pal U (2018) Loop descriptor: local optimal-oriented pattern. IEEE Signal Process Lett 25:635–639
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
Dora L, Agrawal S, Rutuparna P, Abraham A (2017) An evolutionary single Gabor kernel based filter approach to face recognition. Elsevier Engineering Applications of Artificial Intelligence 62:286–301
Dumitrescu CM, Dumitrache I (2019) Combining neural networks and global Gabor features in a hybrid face recognition system. In: 22nd international conference on control systems and computer science (CSCS), pp 216–222
Dumitrescu C-M, Dumitrache I (2019) Combining deep learning technologies with multi-level Gabor features for facial recognition in biometric automated systems. Stud Inform Control 28. https://doi.org/10.24846/v28i2y201910
El khadiri I, Chahi A, El merabet Y, Ruichek Y, Touahni R (2018) Local directional ternary pattern: A New texture descriptor for texture classification. Comput Vis Image Underst 169:14–27
El-merabet Y, Ruichek Y, Idrissiab AE (2019) Attractive-and-repulsive center-symmetric local binary patterns for texture classification. Eng Appl Artif Intell 78:158–172
Fathi A, Alirezazadeh P, Abdali-Mohammadi F (2016) A new global-Gabor-Zernike feature descriptor and its application to face recognition. J Vis Commun Image Represent 38:65–72
Feret image database. (2003) Retrieved from http://www.nist.gov/itl/iad/ig/colorferet.cfm
Fuentes-Hurtado F, Diego-Mas JA, Naranjo V, Alcañiz M (2019) Automatic classification of human facial features based on their appearance. PLoS ONE 14:1–10
Guo G, Zhang N (2019) A survey on deep learning based face recognition. Comput Vis Image Underst 189:102–139
Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann
He F, Liu Y, Zhu X, Huang C, Han Y, Dong H (2014) Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters. J Adv Signal Process 95:1–17
Huang P, Gao G, Qian C (2017) Fuzzy linear regression discriminant projection for face recognition. IEEE Access 7(1–10):4340–4349
Kamaruzamana F, AkraminShafie A (2016) Recognizing faces with normalized local Gabor features and spiking neuron patterns. Pattern Recogn 53:102–115
Kas M, El-merabet Y, Ruichek Y, Messoussi R (2020) A comprehensive comparative study of handcrafted methods for face recognition LBP-like and non LBP operators. Multimed Tools Appl 79:375–413
Katoch S, Chauhan SS, Kumar VA (2020) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80:8091–8126
Khana S, Hussainb M, Aboalsamhb H, Mathkourb H, Bebisc G, Zakariah M (2016) Optimized Gabor features for mass classification in mammography. Appl Soft Comput 44
Kola DGR, Samayamantula SK (2021) A novel approach for facial expression recognition using local binary pattern with adaptive window. Multimed Tools Appl 80:2243–2262
Krishna S, Balasubramanian V, Black J, Panchanathan S (2010) Person-specific characteristic feature selection for face recognition. In: Biometrics: theory, methods, and applications. Jone Wiley & Sons, pp 113–141
Li L, Gao J, Ge H (2016) A new face recognition method via semi-discrete decomposition for one sample problem. Optik 127:7408–7417
Li M, Yu X, Ryu KH, Lee S, Theera-Umpon N (2018) Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition. Clust Comput 21:1117–1126
Lia C, Huang Y, Xue Y (2019) Dependence structure of Gabor wavelets based on copula for face recognition. Expert Syst Appl 137:453–470
Lin W, Hasenstab K, Cunha GM, Schwartzman A (2020) Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment. Sci Rep 10:1–11
Markus Weber image database. (1999) Retrieved from http://www.vision.caltech.edu/Image_Datasets/faces/faces.tar
Masi I, Chang F-J, Choi J (2019) Learning pose-aware models for pose-invariant face recognition in the wild. IEEE Trans Pattern Anal Mach Intell 41:379–393
Masi I, Trần AT, Sahin G, Medioni G (2019) Face-specific data augmentation for unconstrained face recognition. Int J Comput Vis 127:642–667
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Mohammadian Fini R, Mahlouji M, Shahidinejad A (2020) Multi-view face detection in open environments using Gabor features and neural networks. J AI Data Mining 8:461–470
Mohammadian Fini R, Mahlouji M, Shahidinejad A (2022) Real-time face detection using circular sliding of the Gabor energy and neural networks. SIViP 16:1081–1089. https://doi.org/10.1007/s11760-021-02057-3
Moussa M, Douik A, HMILA M (2018) A Novel face recognition approach based on genetic algorithm optimization. Stud Inform Control:27
Muinuddin K, Mohammed CS, Kumar S, Gandikota P (2015) 2D Gabor filter for surface defect detection using GA and PSO optimization techniques. Adv Model Anal B 58:67–83
Ouarda W, Trichili H, Alimi AM, Solaiman B (2014) Face recognition based on geometric features using support vector machines. In: 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp 89–95
Ouslimani F, Ouslimani A, Ameur Z (2019) Rotation-invariant features based on directional coding for texture classification. Neural Comput & Applic 31:6393–6400
Park YS, Lek S (2016) Artificial neural networks: multilayer perceptron for ecological modeling. In: Jørgensen SE (ed) Developments in environmental modeling, pp 123–140
Peng C, Wang N, Li J, Xinbo G (2019) DLFace: deep local descriptor for cross-modality face recognition. Pattern Recogn 90:161–171
Perez CA, Leonardo CA, Castillo LE (2011) Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda. Pattern Recogn 44:951–963
Pornntiwa P, Okafor E, Groefsema M, He S, Schomaker LRB, Wiering MA (2020) One-vs-one classification for deep neural networks. Pattern Recogn 108
Su Y, Shan S, Chen X, Gao W (2009) Hierarchical ensemble of global and local classifiers for face recognition. IEEE Trans Image Process 18:1885–1896
Tong L, Wong W, Kwong CK (2016) Differential evolution-based optimal Gabor filter model for fabric inspection. Neurocomputing 173:1386–1401
Wang M, Deng W (2021) Deep face recognition: a survey. Neurocomputing 429:215–244
Xie X, Lam K-M (2006) Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image. IEEE Trans Image Process 15:2481–2492
Xu Y, Yan W, Yang G, Luo J, Li T, He J (2020) CenterFace: joint face detection and alignment using face as point. Sci Program 2020:1–8
Yang X-S (2017) Optimization. In: Engineering mathematics with examples and applications, pp 267–283
Zangeneh E, Rahmati M, Mohsenzadeh Y (2020) Low resolution face recognition using a two-branch deep convolutional neural network architecture. Expert Syst Appl 139:1–20
Zhou N, Constantinides AG, Huang G, Zhang S (2018) Face recognition based on an improved center symmetric local binary pattern. Neural Computing and Applications volume 30:3791–3797
Zhu N, Yu Z, Kou C (2020) A new deep neural architecture search pipeline for face recognition. IEEE Access 8:91303–91310
Zou J, Ji Q, Nagy G (2007) A comparative study of local matching approach for face recognition. IEEE Trans Image Process 16:2617–2628
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Mohammadian Fini, R., Mahlouji, M. & Shahidinejad, A. Performance improvement in face recognition system using optimized Gabor filters. Multimed Tools Appl 81, 38375–38408 (2022). https://doi.org/10.1007/s11042-022-13167-6
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DOI: https://doi.org/10.1007/s11042-022-13167-6