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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, 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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Cites
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
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
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
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
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
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
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
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
SphereFace: Deep Hypersphere Embedding for Face Recognition
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.713 · Cited by 2,280
Zhang, J., Marszałek, M., Lazebnik, S., & Schmid, C. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-9794-4 · Cited by 1,962
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
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
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
Horner, J. L., & Gianino, P. D. (1984) · Applied Optics · doi:10.1364/ao.23.000812 · Cited by 866
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
Kernel PCA for novelty detection
Hoffmann, H. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.07.009 · Cited by 776
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 610
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
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
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
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
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
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 279
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 212
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
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
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
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
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 170
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
Face Recognition Methods & ApplicationsPreprint
Parmar, D. N., & Mehta, B. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.0485 · Cited by 158
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
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
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
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
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
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 98
Sun, J., Fu, Y., Li, S., He, J., Xu, C., & Tan, L. (2018) · Journal of Sensors · doi:10.1155/2018/8580959 · Cited by 94
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 93
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
Arashloo, S. R., & Kittler, J. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2359587 · Cited by 83
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
Juefei-Xu, F., Luu, K., & Savvides, M. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2468173 · Cited by 74
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
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
A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques
Shah, J. H., Sharif, M., Raza, M., & Azeem, A. (2013) · Cited by 69
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 67
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
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 63
Self-citationExactly 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 61
On 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
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
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
Javidi, B., Li, J., & Tang, Q. (1995) · Applied Optics · doi:10.1364/ao.34.003950 · Cited by 57
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
Understanding Correlation Techniques for Face Recognition: From Basics to Applications
Alfalou, A., & Brosseau, C. (2010) · InTech eBooks · doi:10.5772/8935 · Cited by 54
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 54
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
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
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
Self-citationOuerhani, Y., Jridi, M., Alfalou, A., & Brosseau, C. (2012) · Optics Communications · doi:10.1016/j.optcom.2012.09.074 · Cited by 47
Self-citationOptimizing Deep CNN Architectures for Face Liveness Detection
Koshy, R., & Mahmood, A. (2019) · Entropy · doi:10.3390/e21040423 · Cited by 44
Cho, 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
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
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
Cascade classifier for face detection
Yang, H., & Wang, X. A. (2016) · Journal of Algorithms & Computational Technology · doi:10.1177/1748301816649073 · Cited by 35
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
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
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
Seibold, C., Samek, W., Hilsmann, A., & Eisert, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04265 · Cited by 30
Alfalou, A., Brosseau, C., Katz, P., & Alam, M. S. (2012) · Optics Letters · doi:10.1364/ol.37.001562 · Cited by 29
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
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
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
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
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
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
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
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
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
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
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
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
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
Self-citationOuanan, H., Ouanan, M., & Aksasse, B. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.01.105 · Cited by 19
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
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
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
"Face recognition based on composite correlation filters: analysis of their performances"
Léonard, I., Alfalou, A., & Brosseau, C. (2012) · HAL · Cited by 17
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
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
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
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
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
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
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
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
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
Self-citationNguyen, D. T., Pham, T. D., Lee, M. B., & Park, K. R. (2019) · Sensors · doi:10.3390/s19020410 · Cited by 15
Spectral 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
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
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
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
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
Face recognition performance using linear discriminant analysis and deep neural networks
Bajrami, X., Gashi, B., & Murturi, I. (2018) · International Journal of Applied Pattern Recognition · doi:10.1504/ijapr.2018.094818 · Cited by 13
Automatic landmark detection and 3D Face data extraction
Boukamcha, H., Hallek, M., Smach, F., & Atri, M. (2016) · Journal of Computational Science · doi:10.1016/j.jocs.2016.11.015 · Cited by 12
Self-citationA Fast HOG Descriptor Using Lookup Table and Integral ImagePreprint
Huang, C., & Huang, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06256 · Cited by 12
An improved hybrid approach to face recognition by fusing local and global discriminant features
Sing, J. K., Chowdhury, S., Basu, D. K., & Nasipuri, M. (2012) · International Journal of Biometrics · doi:10.1504/ijbm.2012.046245 · Cited by 11
Optimize Approach to Voice Recognition Using IoT
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
Optimization of decision making for face recognition based on nonlinear correlation plane
Alfalou, A., Brosseau, C., & Kaddah, W. (2015) · Optics Communications · doi:10.1016/j.optcom.2015.01.017 · Cited by 10
Self-citationFace, Age and Gender Recognition Using Local Descriptors
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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
Oravec, M. (2010) · InTech eBooks · doi:10.5772/207 · Cited by 9
Image Feature Extraction using Non Linear Principle Component Analysis
Sivasathya, M., & Joans, S. M. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.06.114 · Cited by 8
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Self-citationFisherface vs. Eigenface in the Dual-tree Complex Wavelet Domain
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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
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
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
Spatial Bag of Features Learning for Large Scale Face Image Retrieval
Passalis, N., & Tefas, A. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-47898-2_2 · Cited by 7
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
Soft Biometrics: Gender Recognition from Unconstrained Face Images Using Local Feature Descriptor
Arigbabu, O. A., Ahmad, S. M. S., Adnan, W. A. W., & Mahmood, S. (2015) · Journal of Information and Communication Technology · doi:10.32890/jict2015.14.7 · Cited by 7
Face Recognition Based on Nonlinear DCT Discriminant Feature Extraction Using Improved Kernel DCV
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
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
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
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
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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
Flagged first, then by citations
Research works printed but not linked: 3
- Perlibakas, V. (2006). Face recognition using principal component analysis and log-gabor filters. arXiv.
- Sufyanu, 2016, Enhanced Face Recognition Using Discrete Cosine Transform, Eng. Lett., 24, 52
- Ouerhani, 2013, Graphics processor unit implementation of correlation technique using a segmented phase only composite filter, Opt. Commun., 289, 33, 10.1016/j.optcom.2012.09.074 · DOI
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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
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 133
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Abdulateef, S. K., & Salman, M. D. (2021) · Iraqi Journal for Electrical And Electronic Engineering · doi:10.37917/ijeee.17.2.18 · Cited by 78
Metadata
- 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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