Face Recognition Systems: A Survey
Open accessAbstract
Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.
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The presence of noise in images can act as a deterrent in the course of various tasks in image processing, such as facial recognition, form processing, and environmental remote sensing.
“The presence of noise in images can act as a deterrent in the course of various tasks in image processing, like facial recognition [1], form processing [2], environmental remote sensing [3], etc.”
From Median Filter Aided CNN Based Image Denoising: An Ensemble Approach (2021) · cites this paper as [1]
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Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300 · see citing works
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267 · see citing works
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733 · see citing works
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882 · see citing works
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542 · see citing works
Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479 · see citing works
A Discriminative Feature Learning Approach for Deep Face Recognition
Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077 · see citing works
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239 · see citing works
SphereFace: Deep Hypersphere Embedding for Face Recognition
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.713 · Cited by 3,128 · see citing works
Zhang, J., Marszałek, M., Lazebnik, S., & Schmid, C. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-9794-4 · Cited by 1,929 · see citing works
Alahi, A., Ortiz, R., & Vandergheynst, P. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247715 · Cited by 1,791 · see citing works
Signal detection by complex spatial filtering
Lugt, A. V. (1964) · IEEE Transactions on Information Theory · doi:10.1109/tit.1964.1053650 · Cited by 1,606 · see citing works
BRIEF: Computing a Local Binary Descriptor Very Fast
Calonder, M., Lepetit, V., Özuysal, M., Trzcinski, T. P., Strecha, C., & Fua, P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.222 · Cited by 867 · see citing works
Horner, J. L., & Gianino, P. D. (1984) · Applied Optics · doi:10.1364/ao.23.000812 · Cited by 866 · see citing works
A Technique for Optically Convolving Two Functions
Weaver, C. S., & Goodman, J. W. (1966) · Applied Optics · doi:10.1364/ao.5.001248 · Cited by 845 · see citing works
Kernel PCA for novelty detection
Hoffmann, H. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.07.009 · Cited by 776 · see citing works
Face recognition using kernel direct discriminant analysis algorithms
Lu, J., Plataniotis, K. N., & Venetsanopoulos, A. N. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2002.806629 · Cited by 608 · see citing works
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
Mian, A. S., Bennamoun, M., & Owens, R. A. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1105 · Cited by 469 · see citing works
Robust Face Recognition via Multimodal Deep Face Representation
Ding, C., & Tao, D. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2477042 · Cited by 449 · see citing works
Fisher Vector Faces in the Wild
Simonyan, K., Parkhi, O., Vedaldi, A., & Zisserman, A. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.8 · Cited by 446 · see citing works
Partial Face Recognition: Alignment-Free Approach
Liao, S., Jain, A. K., & Li, S. Z. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.191 · Cited by 320 · see citing works
Face recognition based on convolutional neural network
Coşkun, M., Uçar, A., Yıldırım, Ö., & Demir, Y. (2017) · International Conference on Modern Electrical and Energy Systems (MEES) · doi:10.1109/mees.2017.8248937 · Cited by 295 · see citing works
Rotating your face using multi-task deep neural network
Yim, J., Jung, H., Yoo, B., Choi, C., Park, D., & Kim, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298667 · Cited by 280 · see citing works
Probabilistic Models for Inference about Identity
Li, P., Fu, Y., Mohammed, U., Elder, J. H., & Prince, S. J. D. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.104 · Cited by 215 · see citing works
A fingerprint and finger-vein based cancelable multi-biometric system
Yang, W., Wang, S., Hu, J., Zheng, G., & Valli, C. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.01.026 · Cited by 210 · see citing works
Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild
Cui, Z., Li, W., Xu, D., Shan, S., & Chen, X. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.456 · Cited by 196 · see citing works
Face Recognition using Local Quantized Patterns
Hussain, S. U., Napoléon, T., & Jurie, F. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.99 · Cited by 190 · see citing works
Fast High Dimensional Vector Multiplication Face Recognition
Barkan, O., Weill, J., Wolf, L., & Aronowitz, H. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.246 · Cited by 173 · see citing works
Frequency Domain Acceleration of Convolutional Neural Networks on CPU-FPGA Shared Memory System
Zhang, C., & Prasanna, V. K. (2017) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/3020078.3021727 · Cited by 164 · see citing works
Deep face recognition using imperfect facial data
Elmahmudi, A. A., & Ugail, H. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.04.025 · Cited by 161 · see citing works
Face Recognition Methods & ApplicationsPreprint
Parmar, D. N., & Mehta, B. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.0485 · Cited by 158 · see citing works
A survey of local feature methods for 3D face recognition
Soltanpour, S., Boufama, B., & Wu, Q. M. J. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.08.003 · Cited by 148 · see citing works
Face Verification Using the LARK Representation
Seo, H. J., & Milanfar, P. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2159205 · Cited by 144 · see citing works
Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation
González-Sosa, E., Fiérrez, J., Vera-Rodríguez, R., & Alonso‐Fernandez, F. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2807791 · Cited by 129 · see citing works
Local Higher-Order Statistics (LHS) for Texture Categorization and Facial Analysis
Sharma, G., Hussain, S. U., & Jurie, F. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33786-4_1 · Cited by 122 · see citing works
Face recognition using SURF features
Du, G., Su, F., & Cai, A. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.832636 · Cited by 120 · see citing works
Component-Based Representation in Automated Face Recognition
Bonnen, K., Klare, B. F., & Jain, A. K. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2226580 · Cited by 94 · see citing works
Local binary pattern network: A deep learning approach for face recognition
Xi, M., Chen, L., Polajnar, D., & Tong, W. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532955 · Cited by 92 · see citing works
Sun, J., Fu, Y., Li, S., He, J., Xu, C., & Tan, L. (2018) · Journal of Sensors · doi:10.1155/2018/8580959 · Cited by 89 · see citing works
Efficient processing of MRFs for unconstrained-pose face recognition
Arashloo, S. R., & Kittler, J. (2013) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2013.6712721 · Cited by 88 · see citing works
Arashloo, S. R., & Kittler, J. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2359587 · Cited by 83 · see citing works
Relaxed local ternary pattern for face recognition
Ren, J., Jiang, X., & Yuan, J. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738759 · Cited by 76 · see citing works
Juefei-Xu, F., Luu, K., & Savvides, M. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2468173 · Cited by 74 · see citing works
A Comparative Evaluation of Well-known Feature Detectors and Descriptors
Işık, Ş. (2014) · International Journal of Applied Mathematics Electronics and Computers · doi:10.18100/ijamec.60004 · Cited by 73 · see citing works
Automated detection of elephants in wildlife video
Zeppelzauer, M. (2013) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2013-46 · Cited by 72 · see citing works
A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques
Shah, J. H., Sharif, M., Raza, M., & Azeem, A. (2013) · Cited by 69 · see citing works
Face recognition under varying expressions and illumination using particle swarm optimization
Khan, S. A., Ishtiaq, M., Nazir, M., & Shaheen, M. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.08.005 · Cited by 66 · see citing works
Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors
Beli, I. K., & Guo, C. (2017) · Journal of Imaging · doi:10.3390/jimaging3030037 · Cited by 64 · see citing works
New perspectives in face correlation research: a tutorial
Wang, Q., Alfalou, A., & Brosseau, C. (2017) · Advances in Optics and Photonics · doi:10.1364/aop.9.000001 · Cited by 62 · see citing works
Self-citationOn the use of kernel PCA for feature extraction in speech recognition
Lima, A. A. D., Zen, H., Nankaku, Y., Miyajima, C., Tokuda, K., & Kitamura, T. (2003) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2003-704 · Cited by 60 · see citing works
HW/SW Co-Design of the HOG algorithm on a Xilinx Zynq SoC
Rettkowski, J., Boutros, A., & Göhringer, D. (2017) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2017.05.005 · Cited by 58 · see citing works
Exactly Robust Kernel Principal Component Analysis
Fan, J., & Chow, T. W. S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2909686 · Cited by 58 · see citing works
Automatic face recognition system based on the SIFT features
Lenc, L., & Král, P. (2015) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2015.01.014 · Cited by 57 · see citing works
Javidi, B., Li, J., & Tang, Q. (1995) · Applied Optics · doi:10.1364/ao.34.003950 · Cited by 57 · see citing works
Non-uniform patch based face recognition via 2D-DWT
Huang, Z., Li, W., Shang, J., Wang, J., & Zhang, T. (2015) · Image and Vision Computing · doi:10.1016/j.imavis.2014.12.005 · Cited by 54 · see citing works
Understanding Correlation Techniques for Face Recognition: From Basics to Applications
Alfalou, A., & Brosseau, C. (2010) · InTech eBooks · doi:10.5772/8935 · Cited by 54 · see citing works
Self-citationA hybrid technique for gender classification with SLBP and HOG features
Annalakshmi, M., Roomi, S. M. M., & Naveedh, A. S. (2018) · Cluster Computing · doi:10.1007/s10586-017-1585-x · Cited by 52 · see citing works
Multi-subregion based correlation filter bank for robust face recognition
Yan, Y., Wang, H., & Suter, D. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.05.004 · Cited by 49 · see citing works
Two Novel Detector-Descriptor Based Approaches for Face Recognition Using SIFT and SURF
Vinay, A., Hebbar, D., Shekhar, V. S., Murthy, K. N. B., & Natarajan, S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.10.070 · Cited by 48 · see citing works
Pose invariant face recognition: 3D model from single photo
Napoléon, T., & Alfalou, A. (2016) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2016.06.019 · Cited by 48 · see citing works
Self-citationOuerhani, Y., Jridi, M., Alfalou, A., & Brosseau, C. (2012) · Optics Communications · doi:10.1016/j.optcom.2012.09.074 · Cited by 47 · see citing works
Self-citationCho, S., Baek, N. R., Kim, M. C., Koo, J., Kim, J. H., & Park, K. (2018) · Italian National Conference on Sensors · doi:10.3390/s18092995 · Cited by 44 · see citing works
Optimizing Deep CNN Architectures for Face Liveness Detection
Koshy, R., & Mahmood, A. (2019) · Entropy · doi:10.3390/e21040423 · Cited by 42 · see citing works
A new kernel Fisher discriminant algorithm with application to face recognition
Yang, J. J., Frangi, A. F., & Yang, J.-Y. (2003) · Neurocomputing · doi:10.1016/s0925-2312(03)00444-2 · Cited by 41 · see citing works
An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets
Cho, H., Roberts, R. G., Jung, B., Choi, O., & Moon, S. B. (2014) · International Journal of Advanced Robotic Systems · doi:10.5772/58473 · Cited by 37 · see citing works
Cascade classifier for face detection
Yang, H., & Wang, X. A. (2016) · Journal of Algorithms & Computational Technology · doi:10.1177/1748301816649073 · Cited by 35 · see citing works
A new pose invariant face recognition system using PCA and ANFIS
Sharma, R., & Patterh, M. S. (2015) · Optik · doi:10.1016/j.ijleo.2015.08.205 · Cited by 33 · see citing works
A Novel Face Recognition Approach Based on Genetic Algorithm Optimization
Moussa, M., Hmila, M., & Douik, A. (2018) · Studies in Informatics and Control · doi:10.24846/v27i1y201813 · Cited by 31 · see citing works
CNN-Based Multimodal Human Recognition in Surveillance Environments
Koo, J. H., Cho, S. W., Baek, N. R., Kim, M. C., & Park, K. R. (2018) · Sensors · doi:10.3390/s18093040 · Cited by 30 · see citing works
Seibold, C., Samek, W., Hilsmann, A., & Eisert, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04265 · Cited by 30 · see citing works
Alfalou, A., Brosseau, C., Katz, P., & Alam, M. S. (2012) · Optics Letters · doi:10.1364/ol.37.001562 · Cited by 29 · see citing works
Self-citationRobust Face Recognition by Computing Distances From Multiple Histograms of Oriented Gradients
Karaaba, M. F., Surinta, O., Schomaker, L., & Wiering, M. (2015) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2015.39 · Cited by 29 · see citing works
An FPGA-based accelerator for Fourier Descriptors computing for color object recognition using SVM
Smach, F., Mitéran, J., Atri, M., Dubois, J., Abid, M., & Gauthier, J.-P. (2007) · Journal of Real-Time Image Processing · doi:10.1007/s11554-007-0065-6 · Cited by 28 · see citing works
Self-citationBisecting k-means clustering based face recognition using block-based bag of words model
Zhao, C., Li, X., & Cang, Y. (2015) · Optik · doi:10.1016/j.ijleo.2015.04.068 · Cited by 28 · see citing works
Fast face recognition approach using a graphical processing unit “GPU”
Ouerhani, Y., Jridi, M., & Alfalou, A. (2010) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2010.5548545 · Cited by 27 · see citing works
Self-citationHybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis
Fathima, A. A., Ajitha, S., Vaidehi, V., Hemalatha, M., Karthigaiveni, R., & Kumar, R. (2015) · IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS) · doi:10.1109/cgvis.2015.7449925 · Cited by 26 · see citing works
A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method
Kamencay, P., Jarina, R., Benčo, M., Hudec, R., Zachariášová, M., & Hlubík, J. (2013) · Brno University of Technology Digital Library (Brno University of Technology) · Cited by 26 · see citing works
A novel face detection approach using local binary pattern histogram and support vector machine
Kortli, Y., Jridi, M., Falou, A. A., & Atri, M. (2018) · IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) · doi:10.1109/aset.2018.8379829 · Cited by 26 · see citing works
Self-citationFace Verification and Recognition for Digital Forensics and Information Security
Amato, G. M., Falchi, F., Gennaro, C., Massoli, F. V., Passalis, N., Tefas, A., Trivilini, A., & Vairo, C. (2019) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs.2019.8757511 · Cited by 26 · see citing works
One lens optical correlation: application to face recognition
Jridi, M., Napoléon, T., & Alfalou, A. (2018) · Applied Optics · doi:10.1364/ao.57.002087 · Cited by 25 · see citing works
Self-citationA comparative study of CFs, LBP, HOG, SIFT, SURF, and BRIEF techniques for face recognition
Alfalou, A., Jridi, M., Kourtli, Y., & Atri, M. (2018) · Pattern Recognition and Tracking · doi:10.1117/12.2309454 · Cited by 24 · see citing works
Self-citationFace Retrieval Based On Local Binary Pattern and Its Variants: A Comprehensive Study
Khoi, P., Huu, L., & Hoai, V. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070632 · Cited by 22 · see citing works
A new nonlinear feature extraction method for face recognition
Pang, Y., Liu, Z., & Yu, N. (2005) · Neurocomputing · doi:10.1016/j.neucom.2005.07.005 · Cited by 21 · see citing works
Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques
Benarab, D., Napoléon, T., Alfalou, A., Verney, A., & Hellard, P. (2015) · Optics Communications · doi:10.1016/j.optcom.2015.07.056 · Cited by 19 · see citing works
Self-citationOuanan, H., Ouanan, M., & Aksasse, B. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.01.105 · Cited by 19 · see citing works
Napoléon, T., & Alfalou, A. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2051267 · Cited by 18 · see citing works
Self-citationNonlinear Face Recognition Based on Maximum Average Margin Criterion
Zhang, B., Chen, X., Shan, S., & Gao, W. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.247 · Cited by 18 · see citing works
Automatic Attendance System Using Face Recognition Technique
Agarwal, R., Jain, R., Regunathan, R., & Kumar, C. S. P. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1610-4_53 · Cited by 18 · see citing works
"Face recognition based on composite correlation filters: analysis of their performances"
Léonard, I., Alfalou, A., & Brosseau, C. (2012) · HAL · Cited by 17 · see citing works
Self-citationDeep CNNs with Robust LBP Guiding Pooling for Face Recognition
Ma, Z., Ding, Y., Li, B., & Yuan, X. (2018) · Sensors · doi:10.3390/s18113876 · Cited by 17 · see citing works
A novel face recognition system based on combining eigenfaces with fisher faces using wavelets
Bodapati, J. D., Veeranjaneyulu, N., & Kolli, V. K. K. (2010) · Procedia Computer Science · doi:10.1016/j.procs.2010.11.007 · Cited by 16 · see citing works
Road mark recognition using HOG-SVM and correlation
Alfalou, A., Ouerhani, Y., & Brosseau, C. (2017) · Optics and Photonics for Information Processing · doi:10.1117/12.2273304 · Cited by 16 · see citing works
Self-citationA SVM Face Recognition Method Based on Optimized Gabor Features
Shen, L., Bai, L., & Ji, Z. (2007) · Visual .. · doi:10.1007/978-3-540-76414-4_17 · Cited by 15 · see citing works
FPGA implementation of 2D cross-correlation for real-time 3D tracking of deformable surfaces
HajiRassouliha, A., Gamage, T. P. B., Parker, M. D., Nash, M. P., Taberner, A. J., & Nielsen, P. M. F. (2013) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2013.6727067 · Cited by 15 · see citing works
A PCA-based Face Recognition Method by Applying Fast Fourier Transform in Pre-processing
Dehai, Z., Da, D., Li, J., & Liu, Q. (2013) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/icmt-13.2013.141 · Cited by 15 · see citing works
Nonlinear feature extraction approaches with application to face recognition over large databases
Vankayalapati, H. D., & Kyamakya, K. (2009) · Joint International Workshop on Nonlinear Dynamics and Synchronization and International Symposium on Theoretical Electrical Engineering (INDS & ISTET) · doi:10.1109/inds.2009.5227967 · Cited by 15 · see citing works
Feature Correlation Filter for Face Recognition
Zhu, X., Liao, S., Lei, Z., Liu, R., & Li, S. Z. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74549-5_9 · Cited by 15 · see citing works
Optical image authentication scheme using dual polarization decoding configuration
Wang, Q., Xiong, D., Alfalou, A., & Brosseau, C. (2018) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2018.09.008 · Cited by 15 · see citing works
Self-citationSpectral optimized asymmetric segmented phase-only correlation filter
Léonard, I., Alfalou, A., & Brosseau, C. (2012) · Applied Optics · doi:10.1364/ao.51.002638 · Cited by 14 · see citing works
Self-citationAn Efficient ORB based Face Recognition framework for Human-Robot Interaction
Vinay, A., Cholin, A. S., Bhat, A. D., Murthy, K. N. B., & Natarajan, S. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.07.095 · Cited by 14 · see citing works
Performance Study of LDA and KFA for Gabor Based Face Recognition System
Vinay, A., Shekhar, V. S., Murthy, K. N. B., & Natarajan, S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.07.493 · Cited by 14 · see citing works
Multimodal Biometric Recognition System Based on Nonparametric Classifiers
Gowda, H. D. S., Kumar, G. H., & Imran, M. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-13-2514-4_23 · Cited by 14 · see citing works
Nguyen, D. T., Pham, T. D., Lee, M. B., & Park, K. R. (2019) · Sensors · doi:10.3390/s19020410 · Cited by 14 · see citing works
A comparative study of GOM, uLBP, VLC and fractional Eigenfaces for face recognition
Ghorbel, A., Tajouri, I., Aydi, W., & Masmoudi, N. (2016) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas.2016.7880143 · Cited by 13 · see citing works
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Sing, J. K., Chowdhury, S., Basu, D. K., & Nasipuri, M. (2012) · International Journal of Biometrics · doi:10.1504/ijbm.2012.046245 · Cited by 11 · see citing works
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Patel, N. P., & Kale, A. (2018) · 2018 International Conference On Advances in Communication and Computing Technology (ICACCT) · doi:10.1109/icacct.2018.8529622 · Cited by 11 · see citing works
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Su, C., Yan, Y., Chen, S., & Wang, H. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296993 · Cited by 9 · see citing works
Oravec, M. (2010) · InTech eBooks · doi:10.5772/207 · Cited by 9 · see citing works
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Hafez, S. F., Selim, M. M., & Zayed, H. H. (2015) · International Journal of Biometrics · doi:10.1504/ijbm.2015.076138 · Cited by 8 · see citing works
Heflin, B. C., Scheirer, W. J., & Boult, T. E. (2012) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2012.6163013 · Cited by 8 · see citing works
Computation EvaBio: A Tool for Performance Evaluation in Biometrics
Mahier, J., Hemery, B., El-Abed, M., El-Allam, M., Bouhaddaoui, M., & Rosenberger, C. (2011) · HAL · Cited by 7 · see citing works
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Passalis, N., & Tefas, A. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-47898-2_2 · Cited by 7 · see citing works
Multi-feature shape regression for face alignment
Yang, W., Chen, Y.-C., Chung, P., & Yang, J. (2018) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-018-0572-6 · Cited by 7 · see citing works
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Li, S., Yao, Y., Jing, X., Chang, H., Gao, S.-Q., Zhang, D. D., & Jing-yu, Y. A. N. G. (2009) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e92.d.2527 · Cited by 6 · see citing works
Pattern Recognition Algorithms for Cluster Identification Problem
Pratima, D., & Nimmakanti, N. (2013) · International Journal of Computer Science and Informatics · doi:10.47893/ijcsi.2013.1089 · Cited by 6 · see citing works
Shanbhag, S. S., Bargi, S., Manikantan, K., & Ramachandran, S. (2014) · International Conference on Science Engineering and Management Research · doi:10.1109/icsemr.2014.7043552 · Cited by 4 · see citing works
Fusion of covariance matrices of PCA and FLD
Guru, D. S., Suraj, M., & Manjunath, S. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.10.006 · Cited by 3 · see citing works
Face recognition using nonlinear locality preserving with deep networks
Wang, Y., Fei, P., Fan, X., & Li, H. (2015) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2808492.2808558 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [84]Hafez S.F. Selim M.M. Zayed H.H. 2d face recognition system based on selected gabor filters and linear discriminant analysis lda arXiv 2015 1503.03741
- [129]Ouerhani Y. Jridi M. Alfalou A. Brosseau C. Graphics processor unit implementation of correlation technique using a segmented phase only composite filter Opt. Commun. 2013 289 33 44 10.1016/j.optcom.2012.09.074 · DOI
Cited by
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Adjabi, I., Ouahabi, A., Benzaoui, A., & Ahmed, A. T. (2020) · Electronics · doi:10.3390/electronics9081188 · Cited by 567 · see citing works
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Huang, Z.-Y., Chiang, C., Chen, J., Chen, Y.-C., Chung, H.-L., Cai, Y. A., & Hsu, H.-C. (2023) · Scientific Reports · doi:10.1038/s41598-023-35446-4 · Cited by 152 · see citing works
Akbar, J. U. M., Kamarulzaman, S. F., Muzahid, A. J. M., Rahman, M. A., & Uddin, M. (2024) · IEEE Access · doi:10.1109/access.2024.3349418 · Cited by 116 · see citing works
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Zhang, S., Qin, S., Xiao, Y., Liu, Z., Hu, X., Xiao, Z., Huang, D., Han, L., & Ye, X. (2025) · Dalton Transactions · doi:10.1039/d4dt03538k · Cited by 49 · see citing works
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Kalyta, O., Barmak, O., Radiuk, P., & Krak, I. V. (2023) · Applied Sciences · doi:10.3390/app13179890 · Cited by 31 · see citing works
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Bahroun, S., Abed, R., & Zagrouba, E. (2021) · The Visual Computer · doi:10.1007/s00371-021-02324-x · Cited by 26 · see citing works
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“The presence of noise in images can act as a deterrent in the course of various tasks in image processing, like facial recognition [1], form processing [2], environmental remote sensing [3], etc.”
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Zhou, S., Zhou, Z., Wang, C., Liang, Y., Wang, L., Zhang, J., Zhang, J., & Lv, C. (2024) · Applied Sciences · doi:10.3390/app14156824 · Cited by 18 · see citing works
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Najmabadi, M., & Moallem, P. (2021) · Optik · doi:10.1016/j.ijleo.2021.168331 · Cited by 17 · see citing works
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Shang, W., Liu, Z., Wang, S., Ding, L., Shang, X., Zhou, Z., & Wang, L. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122502 · Cited by 15 · see citing works
Aliyu, I., Bomoi, M. A., & Maishanu, M. (2022) · International Journal of Information Engineering and Electronic Business · doi:10.5815/ijieeb.2022.03.04 · Cited by 15 · see citing works
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Chen, Y., Yang, T., Li, C., & Zhang, Y. (2020) · Sensors · doi:10.3390/s20236902 · Cited by 14 · see citing works
A novel algorithmic approach of open eye analysis for drowsiness detection
Pandey, N. N., & Muppalaneni, N. B. (2021) · International Journal of Information Technology · doi:10.1007/s41870-021-00811-x · Cited by 14 · see citing works
Ardebili, A., Latifian, A., Aziz, C. F., BinSaeed, R. H., Alizadeh, S. M., & Kostyrin, E. V. (2022) · Journal of Management & Organization · doi:10.1017/jmo.2022.63 · Cited by 14 · see citing works
Development of Blockchain-based e-Voting System: Requirements, Design and Security Perspective
Faruk, M. J. H., Saha, B., Islam, M., Alam, F., Shahriar, H., Valero, M., Rahman, A., Wu, F., & Alam, Z. (2022) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00132 · Cited by 13 · see citing works
Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
Iqbal, M. A., Jadoon, W., & Kim, S. K. (2024) · Applied Sciences · doi:10.3390/app14125049 · Cited by 16 · see citing works
Shao, Z., Zhang, L., Meng, T., & Zhang, J. (2024) · Electronic Markets · doi:10.1007/s12525-024-00742-y · Cited by 14 · see citing works
Face perception foundations for pattern recognition algorithms
Marcolin, F., Vezzetti, E., & Monaci, M. G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.074 · Cited by 15 · see citing works
Anwarul, S., & Dahiya, S. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.4.043055 · Cited by 13 · see citing works
On Mitigating Hard Clusters for Face Clustering
Chen, Y.-J., Zhong, H., Chen, C., Shen, C., Huang, J., Wang, T., Liang, Y. E., & Sun, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19775-8_31 · Cited by 13 · see citing works
Feature Recognition and Style Transfer of Painting Image Using Lightweight Deep Learning
Tan, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1478371 · Cited by 12 · see citing works
Systematic Literature Review on the Accuracy of Face Recognition Algorithms
Rossi, R., Lazarini, M. A., & Hirama, K. (2022) · EAI Endorsed Transactions on Internet of Things · doi:10.4108/eetiot.v8i30.2346 · Cited by 12 · see citing works
Charoqdouz, E., & Hassanpour, H. (2023) · International Journal of Engineering · doi:10.5829/ije.2023.36.08b.14 · Cited by 12 · see citing works
Wang, M., Zhao, Z., Geng, J., & Jiang, W. R. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3347561 · Cited by 12 · see citing works
Razaq, I. S., & Al-Shukur, B. K. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01424-2 · Cited by 12 · see citing works
Multimodal emotional analysis through hierarchical video summarization and face tracking
Thiruthuvanathan, M. M., & Krishnan, B. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11010-y · Cited by 12 · see citing works
Evaluation of Person Recognition Accuracy based on OpenPose parameters
Badave, H., & Kuber, M. (2021) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs51141.2021.9432108 · Cited by 12 · see citing works
A Survey of Face Recognition based on Convolutional Neural Network
Saragih, R. E., & To, Q. H. (2022) · Indonesian Journal of Information Systems · doi:10.24002/ijis.v4i2.5439 · Cited by 11 · see citing works
Thalluri, L. N., Babburu, K., Madam, A. K., Kumar, K. V. V., Ganesh, G. V., Rajasekhar, K., Guha, K., Mohammad, M. B., Kiran, S. S., Sarma, A. V. S. Y. N., et al. (2024) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-023-00310-1 · Cited by 11 · see citing works
Georgievskaya, A., Tlyachev, T., Kiselev, K., Hillebrand, G. G., Chekanov, K., Danko, D., Golodyayev, A., & Majmudar, G. (2024) · Experimental Dermatology · doi:10.1111/exd.15045 · Cited by 11 · see citing works
Kessler, R., Raja, K. B., Tapia, J. E., & Busch, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304610 · Cited by 11 · see citing works
- Authors
- Kortli, Y., Jridi, M., Al Falou, A., & Atri, M.
- Affiliations
- University of Monastir (TN)
- AI-ED Department, Yncrea Ouest, 20 rue du Cuirassé de Bretagne, 29200 Brest, France
- King Khalid University (SA)
- Corresponding author
- Kortli, Yassin
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-01-07
- Volume
- 20
- Issue
- 2
- Pages
- 342
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7013584
- OpenAlex
- W2999711171
- Cited by
- 623
- References
- 128
- Citations by year
- 2026: 44 · 2025: 113 · 2024: 118 · 2023: 145 · 2022: 107 · 2021: 81 · 2020: 15
- FWCI
- 33.82
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- face recognition, holistic face recognition, face recognition databases, video surveillance, access control, unmanned vehicles, recognition robustness
- MeSH terms
- Facial Recognition, Algorithms, Humans, Pattern Recognition, Automated, Surveys and Questionnaires, Neural Networks, Computer, Databases as Topic, Imaging, Three-Dimensional, Principal Component Analysis
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