A comprehensive overview of feature representation for biometric recognition

The performance of any biometric recognition system heavily dependents on finding a good and suitable feature representation space where observations from different classes are well separated. Unfortunately, finding this proper representation is a challenging problem which has taken a huge interest in machine learning and computer vision communities. In the this paper we present a comprehensive overview of the different existing feature representation techniques. This is carried out by introducing simple and clear taxonomies as well as effective explanation of the prominent techniques. This is intended to guide the neophyte and provide researchers with state-of-the-art approaches in order to help advance the research topic in biometrics.

[1]  Anil K. Jain,et al.  Simultaneous feature selection and clustering using mixture models , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Michel Verleysen,et al.  The Curse of Dimensionality in Data Mining and Time Series Prediction , 2005, IWANN.

[3]  Geoffrey E. Hinton,et al.  Probabilistic sequential independent components analysis , 2004, IEEE Transactions on Neural Networks.

[4]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[5]  Lin Wu,et al.  Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method , 2017, IEEE Transactions on Cybernetics.

[6]  Somaya Al-Máadeed,et al.  Robust gait recognition: a comprehensive survey , 2018, IET Biom..

[7]  Cun-Hui Zhang Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.

[8]  David A. Landgrebe,et al.  Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[9]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[10]  Qinghua Zheng,et al.  Adaptive Unsupervised Feature Selection With Structure Regularization , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Liqing Zhang,et al.  Natural gradient algorithm for blind separation of overdetermined mixture with additive noise , 1999, IEEE Signal Processing Letters.

[12]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[13]  Chengqi Zhang,et al.  Convex Sparse PCA for Unsupervised Feature Learning , 2014, ACM Trans. Knowl. Discov. Data.

[14]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Wm. R. Wright General Intelligence, Objectively Determined and Measured. , 1905 .

[16]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[17]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[18]  Donghui Wang,et al.  A Brief Summary of Dictionary Learning Based Approach for Classification (revised) , 2012, ArXiv.

[19]  Sambit Bakshi,et al.  Security through human-factors and biometrics , 2013, SIN.

[20]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[21]  Yi-Hao Kao,et al.  Learning a factor model via regularized PCA , 2011, Machine Learning.

[22]  Marc Sebban,et al.  A Survey on Metric Learning for Feature Vectors and Structured Data , 2013, ArXiv.

[23]  Arif Mahmood,et al.  Palmprint Identification Using an Ensemble of Sparse Representations , 2018, IEEE Access.

[24]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[25]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[26]  Thomas Hofmann,et al.  Efficient Learning of Sparse Representations with an Energy-Based Model , 2007 .

[27]  Shun-ichi Amari,et al.  Natural Gradient Learning for Over- and Under-Complete Bases in ICA , 1999, Neural Computation.

[28]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[29]  Baoxin Li,et al.  Discriminative K-SVD for dictionary learning in face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[31]  Wu Jigang,et al.  Enhanced Minutiae Extraction for High-Resolution Palmprint Recognition , 2017, Int. J. Image Graph..

[32]  Ahmed Bouridane,et al.  Unsupervised feature selection method for improved human gait recognition , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[33]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[34]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[35]  Qinghua Zheng,et al.  Avoiding Optimal Mean Robust PCA/2DPCA with Non-greedy ℓ1-Norm Maximization , 2016, IJCAI.

[36]  Ahmed Bouridane,et al.  Robust model-free gait recognition by statistical dependency feature selection and Globality-Locality Preserving Projections , 2016, 2016 39th International Conference on Telecommunications and Signal Processing (TSP).

[37]  Shuicheng Yan,et al.  Learning With $\ell ^{1}$-Graph for Image Analysis , 2010, IEEE Transactions on Image Processing.

[38]  Francis R. Bach,et al.  Consistency of the group Lasso and multiple kernel learning , 2007, J. Mach. Learn. Res..

[39]  Hugues Bersini,et al.  A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[40]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[41]  Massimiliano Pontil,et al.  Regularization and statistical learning theory for data analysis , 2002 .

[42]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[43]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[44]  W. Torgerson Multidimensional scaling: I. Theory and method , 1952 .

[45]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[46]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[47]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[48]  Xudong Jiang,et al.  An Ensemble Learning Method Based on Random Subspace Sampling for Palmprint Identification , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[49]  Ahmed Bouridane,et al.  Improved Model-Free Gait Recognition Based on Human Body Part , 2017 .

[50]  Rong Wang,et al.  Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction , 2017, IEEE Transactions on Image Processing.

[51]  Ahmed Bouridane,et al.  Gait recognition based on modified phase-only correlation , 2014, Signal, Image and Video Processing.

[52]  Jean-Philippe Vert,et al.  The group fused Lasso for multiple change-point detection , 2011, 1106.4199.

[53]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[54]  Rick Archibald,et al.  Feature Selection and Classification of Hyperspectral Images With Support Vector Machines , 2007, IEEE Geoscience and Remote Sensing Letters.

[55]  Romain Hérault,et al.  Supervised Music Chord Recognition , 2014, 2014 13th International Conference on Machine Learning and Applications.

[56]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[57]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[58]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[59]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[60]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[61]  Bernhard Schölkopf,et al.  Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..

[62]  John Porrill,et al.  Undercomplete Independent Component Analysis for Signal Separation and Dimension Reduction , 1997 .

[63]  Ahmed Bouridane,et al.  Improved gait recognition based on gait energy images , 2014, 2014 26th International Conference on Microelectronics (ICM).

[64]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[65]  Geoffrey I. Webb,et al.  Encyclopedia of Machine Learning , 2011, Encyclopedia of Machine Learning.

[66]  Xudong Jiang,et al.  Human Body Part Selection by Group Lasso of Motion for Model-Free Gait Recognition , 2016, IEEE Signal Processing Letters.

[67]  YanShuicheng,et al.  Learning with l1-graph for image analysis , 2010 .

[68]  Zoubin Ghahramani,et al.  Unifying linear dimensionality reduction , 2014, 1406.0873.

[69]  Qinghua Zheng,et al.  Simple to Complex Cross-modal Learning to Rank , 2017, Comput. Vis. Image Underst..

[70]  Lina Yao,et al.  Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining , 2017, ACM Trans. Knowl. Discov. Data.

[71]  Michael I. Jordan,et al.  A Direct Formulation for Sparse Pca Using Semidefinite Programming , 2004, SIAM Rev..

[72]  R. Tibshirani,et al.  Sparsity and smoothness via the fused lasso , 2005 .

[73]  Russell H. Lambert,et al.  OVERDETERMINED BLIND SOURCE SEPARATION: USING MORE SENSORS THAN SOURCE SIGNALS IN A NOISY MIXTURE , 2000 .

[74]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[75]  Karl Pearson F.R.S. LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[76]  T. Poggio,et al.  Regression and Classification with Regularization , 2003 .

[77]  Yung C. Shin,et al.  Sparse Multiple Kernel Learning for Signal Processing Applications , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[78]  Yurii Nesterov,et al.  Generalized Power Method for Sparse Principal Component Analysis , 2008, J. Mach. Learn. Res..

[79]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[80]  Xiaojun Chen,et al.  Local Adaptive Projection Framework for Feature Selection of Labeled and Unlabeled Data , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[81]  P. Langley Selection of Relevant Features in Machine Learning , 1994 .

[82]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[83]  Lei Zhang,et al.  Metaface learning for sparse representation based face recognition , 2010, 2010 IEEE International Conference on Image Processing.

[84]  Ahmed Bouridane,et al.  Improved Human Gait Recognition , 2015, ICIAP.

[85]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[86]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[87]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[88]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[89]  Lina Yao,et al.  Diagnosis Code Assignment Using Sparsity-Based Disease Correlation Embedding , 2016, IEEE Transactions on Knowledge and Data Engineering.

[90]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[91]  Huan Liu,et al.  A Probabilistic Approach to Feature Selection - A Filter Solution , 1996, ICML.

[92]  Yi Yang,et al.  A Convex Formulation for Semi-Supervised Multi-Label Feature Selection , 2014, AAAI.

[93]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[94]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[95]  Xudong Jiang,et al.  Palmprint identification using sparse and dense hybrid representation , 2019, Multimedia Tools and Applications.

[96]  Qinghua Zheng,et al.  An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition , 2018, IEEE Transactions on Cybernetics.

[97]  R. Tibshirani,et al.  Sparse Principal Component Analysis , 2006 .

[98]  Hayit Greenspan,et al.  Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification , 2017, IEEE Transactions on Biomedical Engineering.

[99]  Nicu Sebe,et al.  Joint Attributes and Event Analysis for Multimedia Event Detection , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[100]  Qinghua Zheng,et al.  Avoiding Optimal Mean ℓ2,1-Norm Maximization-Based Robust PCA for Reconstruction , 2017, Neural Computation.

[101]  Guillermo Sapiro,et al.  Classification and clustering via dictionary learning with structured incoherence and shared features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[102]  André Elisseeff,et al.  Stability and Generalization , 2002, J. Mach. Learn. Res..

[103]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[104]  Michael Elad,et al.  On the Role of Sparse and Redundant Representations in Image Processing , 2010, Proceedings of the IEEE.

[105]  Jean Ponce,et al.  Sparse Modeling for Image and Vision Processing , 2014, Found. Trends Comput. Graph. Vis..

[106]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[107]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[108]  Larry S. Davis,et al.  Learning a discriminative dictionary for sparse coding via label consistent K-SVD , 2011, CVPR 2011.

[109]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[110]  Dacheng Tao,et al.  Structure Regularized Unsupervised Discriminant Feature Analysis , 2017, AAAI.

[111]  David Zhang,et al.  Fisher Discrimination Dictionary Learning for sparse representation , 2011, 2011 International Conference on Computer Vision.

[112]  SchmidhuberJürgen,et al.  2005 Special Issue , 2005 .

[113]  Erkki Oja,et al.  Independent Component Analysis Aapo Hyvärinen, Juha Karhunen, , 2004 .

[114]  Josef Kittler,et al.  Texture Description by Independent Components , 2002, SSPR/SPR.

[115]  Gian Luca Marcialis,et al.  Palmprint recognition with an efficient data driven ensemble classifier , 2019, Pattern Recognit. Lett..

[116]  Fabian J. Theis,et al.  A geometric algorithm for overcomplete linear ICA , 2004, Neurocomputing.

[117]  Yousef Saad,et al.  Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.