The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
PCA versus LDA
Abstract
In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis). In this communication, we show that this is not always the case. We present our case first by using intuitively plausible arguments and, then, by showing actual results on a face database. Our overall conclusion is that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The dimensional space of the data can be reduced by employing dimensionality reduction techniques such as feature extraction and feature selection.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Martinez and Kak, 2001
Citations
Cites
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,798
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,058
Introduction to statistical pattern recognition (2nd ed.)
Fukunaga, K. (1990) · Academic Press Professional, Inc. eBooks · Cited by 3,802
The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
Martı́nez, A. M. (1998) · CTIT technical reports series · Cited by 2,791
Application of the Karhunen-Loeve procedure for the characterization of human faces
Kirby, M. J. L., & Sirovich, L. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.41390 · Cited by 2,459
Low-dimensional procedure for the characterization of human faces
Sirovich, L., & Kirby, M. J. L. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.000519 · Cited by 2,329
Martı́nez, A. M. (1998) · Medical Entomology and Zoology · Cited by 1,982
Visual learning and recognition of 3-d objects from appearance
Murase, H., & Nayar, S. K. (1995) · International Journal of Computer Vision · doi:10.1007/bf01421486 · Cited by 1,873
Flagged first, then by citations
Research works printed but not linked: 4
- Moon, 1998, Analysis of PCA-based Face Recognition Algorithms
- Murase, 1981, An Improvement of the Autocorrelation Matrix in Pattern Matching Method and Its Application to Handprinted ’HIRAGANA’, Trans. IECE Japan, 64D
- Pentland, Experiments with Eigenfaces
- Weng, 1996, Crescepton and SHOSLIF: Towards Comprehensive Visual Learning, 183
Cited by
Sorted by citations
Items 501–600 of 1,000
LDOP: local directional order pattern for robust face retrieval
Dubey, S. R., & Mukherjee, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08370-x · Cited by 43
An, J., Zhang, X., Zhou, H., & Jiao, L. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2835514 · Cited by 43
DGRU based human activity recognition using channel state information
Bokhari, S. M., Sohaib, S., Khan, A. R., Shafi, M., & Khan, A. U. R. (2020) · Measurement · doi:10.1016/j.measurement.2020.108245 · Cited by 45
Abnormal heart sound classification using phonocardiography signals
Milani, M. G. M., Abas, P. E., Silva, L. C. D., & Nanayakkara, N. D. (2021) · Smart Health · doi:10.1016/j.smhl.2021.100194 · Cited by 43
Facial asymmetry quantification for expression invariant human identification
Liu, Y., Schmidt, K. L., Cohn, J. F., & Weaver, R. (2003) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2002.1004156 · Cited by 45
Human action recognition using Pose-based discriminant embedding
Saghafi, B., & Rajan, D. (2011) · Signal Processing Image Communication · doi:10.1016/j.image.2011.05.002 · Cited by 42
A comparison of PCA, KPCA and LDA for feature extraction to recognize affect in gait kinematics
Karg, M. E., Jenke, R., Seiberl, W., Kühnlenz, K., Schwirtz, A., & BUSS, M. J. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349438 · Cited by 42
Satonkar, S. S. (2012) · IOSR Journal of Engineering · doi:10.9790/3021-021241523 · Cited by 42
Adam, T. W., Ferge, T., Mitschke, S., Streibel, T., Baker, R. R., & Zimmermann, R. (2004) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-004-2935-0 · Cited by 42
Dynamic Image-to-Class Warping for Occluded Face Recognition
Wei, X., Li, C., Lei, Z., Yi, D., & Li, S. Z. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2359632 · Cited by 42
Yuan, L., Zhao, J., Shen, Z., Zhang, Q., Geng, Y., Zheng, C.-H., & Huang, D.-S. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011344 · Cited by 41
Živić, F., Kaplarevic-Malisic, A. M., Grujović, N., Stojanovic, B. S., & Ivanovic, M. R. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.113525 · Cited by 51
Subclass-based multi-task learning for Alzheimer's disease diagnosis
Suk, H., Lee, S., & Shen, D. G. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00168 · Cited by 43
Performance study of cyclostationary based digital modulation classification schemes
Satija, U., Manikandan, M. S., & Ramkumar, B. (2014) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciinfs.2014.7036609 · Cited by 41
Face Biometrics Based on Principal Component Analysis and Linear Discriminant Analysis
Chan, L.-H. (2010) · Journal of Computer Science · doi:10.3844/jcssp.2010.693.699 · Cited by 41
Wang, S., & Liu, S. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms161226237 · Cited by 41
An optimized second order stochastic learning algorithm for neural network training
Liew, S. S., Khalil-Hani, M., & Bakhteri, R. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.076 · Cited by 41
Unsupervised feature extraction by low-rank and sparsity preserving embedding
Zhan, S., Wu, J., Han, N., Wen, J., & Fang, X. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.10.001 · Cited by 41
Makimori, G. Y. F., & Bona, E. (2019) · Food Analytical Methods · doi:10.1007/s12161-019-01443-5 · Cited by 41
Zhao, H., Zhang, J., Jiang, Z., Wei, D., Zhang, X., & Mao, Z. (2019) · Sensors · doi:10.3390/s19112590 · Cited by 41
Analysis of PCA and LDA Features for Facial Expression Recognition Using SVM and HMM Classifiers
Varma, S. L., Shinde, M., & Chavan, S. (2019) · Springer eBooks · doi:10.1007/978-3-030-16848-3_11 · Cited by 41
Xu, Y., Yu, Z., Cao, W., & Chen, C. L. P. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3087517 · Cited by 41
scCDG: A Method Based on DAE and GCN for scRNA-Seq Data Analysis
Wang, H., Zhao, J., Su, Y., & Zheng, C.-H. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3126641 · Cited by 41
Combined subspace method using global and local features for face recognition
Kim, C., Oh, J. Y., & Choi, C. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556212 · Cited by 41
Image representation using Laplacian regularized nonnegative tensor factorization
Wang, C., He, X., Bu, J., Chen, Z., Chen, C., & Guan, Z. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.021 · Cited by 41
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition
Harandi, M. T., Ahmadabadi, M. N., & Araabi, B. N. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0161-5 · Cited by 41
Eigenfaces vs. fisherfaces vs. ICA for face recognition; a comparative study
Sharkas, M., & Elenien, M. A. (2008) · doi:10.1109/icosp.2008.4697276 · Cited by 41
Merits of random forests emerge in evaluation of chemometric classifiers by external validation
Scott, I. M., Lin, W., Liakata, M., Wood, J. E., Vermeer, C. P., Allaway, D., Ward, J. L., Draper, J., Beale, M. H., Corol, D. I., et al. (2013) · Analytica Chimica Acta · doi:10.1016/j.aca.2013.09.027 · Cited by 41
The POSTECH face database (PF07) and performance evaluation
Lee, H., Park, S. S., Kang, B.-N., Shin, J., Lee, J. Y., Je, H.-M., Jun, B., & Kim, D. (2008) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2008.4813378 · Cited by 41
Face expression recognition system based on ripplet transform type II and least square SVM
Kar, N. B., Babu, K. S., Sangaiah, A. K., & Bakshi, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5485-0 · Cited by 51
Improving Face Recognition Rate with Image Preprocessing
Dharavath, K. (2014) · Indian Journal of Science and Technology · doi:10.17485/ijst/2014/v7i8.26 · Cited by 40
Optimizing principal component analysis performance for face recognition using genetic algorithm
Al-Arashi, W. H., Ibrahim, H., & Suandi, S. A. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.08.022 · Cited by 40
A Doubly Weighted Approach for Appearance-Based Subspace Learning Methods
Lu, J., & Tan, Y. (2009) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2009.2035976 · Cited by 40
Bridging the Semantic Gap via Functional Brain Imaging
Hu, X., Li, K., Han, J., Hua, X., Guo, L., & Liu, T. (2011) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2172201 · Cited by 42
Zafar, I., Edirisinghe, E. A., Acar, S., & Bez, H. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.704592 · Cited by 40
A Dynamic Hybrid Projection Approach for Improved Wi-Fi Location Fingerprinting
Fang, S., & Wang, C.-H. (2011) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2011.2107757 · Cited by 40
Selecting Principal Components in a Two-Stage LDA Algorithm
Zhu, M., & Martı́nez, A. M. (2006) · 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.2006.271 · Cited by 40
Learning the sparse representation for classification
Yang, J., Wang, J., & Huang, T. S. (2011) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2011.6012083 · Cited by 41
Zhao, H., Lai, Z., Leung, H., & Zhang, X. (2020) · Information fusion and data science · doi:10.1007/978-3-030-40794-0_5 · Cited by 40
Shabani, H., Mikaeili, M., & Noori, S. M. R. (2016) · Biomedical Engineering Letters · doi:10.1007/s13534-016-0223-5 · Cited by 40
Face recognition-based real-time system for surveillance
Mahdi, F. P., Habib, M., Ahad, M. A. R., Mckeever, S., Moslehuddin, A. S. M., & Vasant, P. (2017) · International Journal of Intelligent Decision Technologies · doi:10.3233/idt-160279 · Cited by 37
Shadan, A. F., Mahat, N. A., Ibrahim, W. A. W., Ariffin, Z., & Ismail, D. (2017) · Journal of Forensic Sciences · doi:10.1111/1556-4029.13512 · Cited by 40
Inference of a compact representation of sensor fingerprint for source camera identification
Li, R., Li, C., & Guan, Y. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.09.027 · Cited by 40
Transferable Linear Discriminant Analysis
Han, N., Wu, J., Fang, X., Wen, J., Zhan, S., Xie, S., & Li, X. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2966746 · Cited by 41
Jointly Heterogeneous Palmprint Discriminant Feature Learning
Fei, L., Zhang, B., Xu, Y. D., Tian, C., Rida, I., & Zhang, D. D. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3066381 · Cited by 40
Appearance-based statistical methods for face recognition
Delač, K., Grgić, M., & Liatsis, P. (2005) · International Symposium ELMAR (ELMAR) · doi:10.1109/elmar.2005.193665 · Cited by 40
Improving a Discriminative Approach to Object Recognition Using Image Patches
Deselaers, T., Keysers, D., & Ney, H. (2005) · Lecture notes in computer science · doi:10.1007/11550518_41 · Cited by 40
Zhao, D., Cao, J., Zhu, X., Zhang, Z., Arun, P. V., Guo, Y., Qian, K., Zhang, L., Zhou, H., & Hu, J. (2022) · Remote Sensing · doi:10.3390/rs14246219 · Cited by 42
Global plus local: A complete framework for feature extraction and recognition
Zhang, D., He, J., Zhao, Y., Luo, Z., & Du, M. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.10.005 · Cited by 39
Two-factor face authentication using matrix permutation transformation and a user password
Kang, J., Nyang, D., & Lee, K.-H. (2014) · Information Sciences · doi:10.1016/j.ins.2014.02.011 · Cited by 39
Averaged Gabor Filter Features for Facial Expression Recognition
Lajevardi, S. M., & Lech, M. (2008) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2008.12 · Cited by 39
Subclass discriminant Nonnegative Matrix Factorization for facial image analysis
Nikitidis, S., Tefas, A., Nikolaidis, N., & Pitas, I. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.04.030 · Cited by 39
Lin, H., Kang, W., Kutsanedzie, F. Y. H., & Chen, Q. (2019) · Food Analytical Methods · doi:10.1007/s12161-019-01617-1 · Cited by 39
Locality adaptive preserving projections for linear dimensionality reduction
Wang, A., Zhao, S., Liu, J., Yang, J., Liu, L., & Chen, G. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113352 · Cited by 39
An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals
Himeur, Y., Alsalemi, A., Bensaali, F., & Amira, A. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22292 · Cited by 39
Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach
Gálvez, A., Diez-Olivan, A., Seneviratne, D., & Galar, D. P. (2021) · Sustainability · doi:10.3390/su13126828 · Cited by 39
Santorelli, A., Porter, E., Kirshin, E., Liu, Y. J., & Popović, M. (2014) · Electromagnetic waves · doi:10.2528/pier13110709 · Cited by 41
Face recognition based on discriminant fractional Fourier feature extraction
Jing, X., Wong, H., & Zhang, D. D. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2006.02.020 · Cited by 39
Parameterized principal component analysis
Gupta, A. K., & Barbu, A. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.01.018 · Cited by 39
Yang, C., & Zhang, H. (2023) · Microchimica Acta · doi:10.1007/s00604-023-06021-5 · Cited by 38
Bayesian optimization of histogram of oriented gradients (HOG) parameters for facial recognition
Bouchene, M. M. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06259-7 · Cited by 43
Graph Regularized Sparsity Discriminant Analysis for face recognition
Lou, S., Zhao, X., Chuang, Y., Yu, H., & Zhang, S. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.04.116 · Cited by 38
A novel locally linear KNN model for visual recognition
Liu, Q., & Liu, C. (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.2015.7298738 · Cited by 38
Manifold discriminant regression learning for image classification
Lu, Y., Lai, Z., Fan, Z., Cui, J., & Zhu, Q. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.031 · Cited by 38
Recognition of armature current of DC generator depending on rotor speed using FFT, MSAF-1 and LDA
Głowacz, A., Glowacz, W., & Głowacz, Z. (2015) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein.2015.1.9 · Cited by 38
Face recognition and facial expression identification using PCA
Meher, S. S., & Maben, P. (2014) · IEEE International Advance Computing Conference · doi:10.1109/iadcc.2014.6779478 · Cited by 38
Modeling cognitive and emotional processes: A novel neural network architecture
Khashman, A. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.07.004 · Cited by 40
Kinship verification in the wild: The first kinship verification competition
Lu, J., Hu, J., Zhou, X., Zhou, J., Castrillón-Santana, M., Lorenzo-Navarro, J., Kou, L., Shang, Y., Bottino, A., & Vieira, T. F. (2014) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2014.6996230 · Cited by 38
Texture based MRI segmentation with a two-stage hybrid neural classifier
Pitiot, A., Toga, A. W., Ayache, N., & Thompson, P. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2002.1007457 · Cited by 38
Trace-Oriented Feature Analysis for Large-Scale Text Data Dimension Reduction
Yan, J., Liu, N., Yan, S., Yang, Q., Fan, W., Wei, W., & Chen, Z. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.34 · Cited by 38
Generalised nearest feature line for subspace learning
Pang, Y., Yuan, Y., & Li, X. (2007) · Electronics Letters · doi:10.1049/el:20072176 · Cited by 38
A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations
Zhang, Y., Feng, T., Wang, S., Dong, R., Yang, J., Su, J., & Wang, B. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.585029 · Cited by 38
Xu, Y., Meng, L., Chen, X., Chen, X., Su, L., Yuan, L., Shi, W. H., & Huang, G. (2021) · Plasma Science and Technology · doi:10.1088/2058-6272/ac071b · Cited by 38
TBDA-Net: A Task-Based Bias Domain Adaptation Network Under Industrial Small Samples
Ren, Y., Liu, J., Zhang, H., & Wang, J. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3141771 · Cited by 38
Incremental LDA Learning by Combining Reconstructive and Discriminative Approaches
Uray, M., Skočaj, D., Roth, P. M., Bischof, H., & Leonardis, A. (2007) · British Machine Vision Conference (BMVC) · doi:10.5244/c.21.44 · Cited by 38
Zhou, S., & Dhupia, J. S. (2020) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2019.12.193 · Cited by 37
RetractedJoint Metric Learning-Based Class-Specific Representation for Image Set Classification
Gao, X., Niu, S., Dong, W. F., Liu, X., Wang, T., Zhu, F., Dong, J., & Sun, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3212703 · Cited by 37
Ganaie, M. A., Sajid, M., Malik, A. K., & Tanveer, M. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3353531 · Cited by 37
Preliminary studies on the Good, the Bad, and the Ugly face recognition challenge problem
Lui, Y. M., Bolme, D. S., Phillips, P. J., Beveridge, J. R., & Draper, B. A. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239209 · Cited by 37
Target recognition in SAR imagery based on local gradient ratio pattern
Yuan, X. D., Tang, T., Xiang, D., Li, Y. X., & Su, Y. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2013.873150 · Cited by 37
Towards automated appliance recognition using an EMF sensor in NILM platforms
Giri, S., Berges, M. E., & Rowe, A. (2013) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.03.004 · Cited by 37
Expression-Invariant Face Recognition With Constrained Optical Flow Warping
Hsieh, C.-K., Lai, S., & Chen, Y. (2009) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2009.2017606 · Cited by 37
Decoding EEG in Cognitive Tasks With Time-Frequency and Connectivity Masks
Li, J., Wang, Y., Zhang, L., Cichocki, A. S., & Jung, T. (2016) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2016.2555952 · Cited by 39
ATM crime detection using image processing integrated video surveillance: a systematic review
Sikandar, T., Ghazali, K. H., & Rabbi, M. F. (2018) · Multimedia Systems · doi:10.1007/s00530-018-0599-4 · Cited by 42
Baig, F., Mehmood, Z., Rashid, M., Javid, M. A., Rehman, A. U., Saba, T., & Adnan, A. (2019) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-019-00237-z · Cited by 37
Kalogiouri, N. P., & Samanidou, V. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-09681-5 · Cited by 37
Sha, J., Fan, M., Cao, B., & Liu, B. (2020) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2020.167516 · Cited by 37
Nascimento, M. H. C., Marcarini, W. D., Folli, G. S., Filho, W. G. D. S., Barbosa, L. L., de Paulo, E. H., Vassallo, P. F., Mill, J. G., Baraúna, V. G., Martin, F. L., et al. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.1c04162 · Cited by 37
A hybrid system for distortion classification and image quality evaluation
Chetouani, A., Beghdadi, A., & Deriche, M. A. (2012) · Signal Processing Image Communication · doi:10.1016/j.image.2012.06.001 · Cited by 37
Li, K., Wu, Y., Yu, N., Li, P., & Li, Y. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.08.131 · Cited by 37
Liu, J., & Chen, S. (2006) · Image and Vision Computing · doi:10.1016/j.imavis.2005.11.007 · Cited by 36
An approach to the de-identification of faces in different poses
Samaržija, B., & Ribarič, S. (2014) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2014.6859758 · Cited by 36
Breast density classification to reduce false positives in CADe systems
Vállez, N., Bueno, G., Déniz, Ó., Dorado, J., Seoane, J. A., Pazos, A., & Pastor, C. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.10.004 · Cited by 36
Pei, J., Huang, Y., Huo, W., Wu, J., Yang, J., & Yang, H. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2555938 · Cited by 36
Sparse locality preserving discriminative projections for face recognition
Zhang, J., Wang, J., & Cai, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.04.051 · Cited by 36
Brindha, G. J., & Subbu, E. S. G. (2017) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2017.2772918 · Cited by 36
Acoustic data condensation to enhance pipeline leak detection
Oh, S. W., Yoon, D.-B., Kim, G. J., Bae, J., & Kim, H. S. (2018) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2017.12.006 · Cited by 37
Fine hyperspectral classification of rice varieties based on attention module 3D-2DCNN
Meng, Y., Ma, Z., Ji, Z., Gao, R. C., & Su, Z. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107474 · Cited by 36
Konaté, A. A., Pan, H., Ma, H., Cao, X., Ziggah, Y. Y., Oloo, M. A., & Khan, N. (2015) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2015.06.035 · Cited by 36
Schuhfried, E., del Pulgar, J. S., Bobba, M., Piro, R., Cappellin, L., Märk, T. D., & Franco Biasioli, A. (2015) · Talanta · doi:10.1016/j.talanta.2015.09.062 · Cited by 36
Metadata
- Authors
- Martinez, A. M., & Kak, A. C.
- Affiliations
- Purdue University West Lafayette (US)
- ORCID
- Kak, A.C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2001
- Volume
- 23
- Issue
- 2
- Pages
- 228-233
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2134262590
- Cited by
- 3,249
- References
- 18
- Citations by year
- 2026: 46 · 2025: 87 · 2024: 107 · 2023: 103 · 2022: 147 · 2021: 148 · 2020: 154 · 2019: 171 · 2018: 172 · 2017: 193 · 2016: 166 · 2015: 186 · 2014: 217 · 2013: 185 · 2012: 171
- FWCI
- 51.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- principal component analysis, linear discriminant analysis, appearance-based object recognition, face recognition, limited training data, dimensionality reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review