Principal component analysis
MPCA has been applied to face recognition, gait recognition, etc. MPCA is further extended to uncorrelated MPCA, non-negative ... component analysis Geometric data analysis Independent component analysis Kernel PCA L1-norm principal component analysis Low- ... which may be seen as the counterpart of principal component analysis for categorical data. Principal component analysis creates ... Principal component analysis (Wikibooks) Principal component regression Singular spectrum analysis Singular value decomposition ...
Eigenface
By collecting many face outlines, principal component analysis can be used to form a basis set of models that encapsulate the ... "Analysis and Comparison of Eigenspace-Based Face Recognition Approaches" (PDF). International Journal of Pattern Recognition ... Face images usually occupy a high-dimensional space and conventional principal component analysis was intractable on such data ... Sirovich and Kirby showed that principal component analysis could be used on a collection of face images to form a set of basis ...
Robust principal component analysis
To be able to correctly retrieve this subspace is crucial in many applications such as face recognition and alignment. It turns ... Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component ... L1-norm principal component analysis Robust PCA Dynamic RPCA Decomposition into Low-rank plus Additive Matrices Low-rank models ... J. Wright; Y. Peng; Y. Ma; A. Ganesh; S. Rao (2009). "Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank ...
Facial recognition system
... locate a face in an image that contains other objects gained traction in the early 1990s with the principal component analysis ... Popular recognition algorithms include principal component analysis using eigenfaces, linear discriminant analysis, elastic ... Three-dimensional data points from a face vastly improve the precision of face recognition. 3D-dimensional face recognition ... "Face Recognition" (PDF). Retrieved June 4, 2008. R. Brunelli and T. Poggio, "Face Recognition: Features versus Templates", IEEE ...
Kernel eigenvoice
This incorporates Kernel principal component analysis, a non-linear version of Principal Component Analysis, to capture higher ... Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice (KEV) is proposed. KEV is a non-linear ... order correlations in order to further explore the speaker space and enhance recognition performance. fMLLR "Kernel Eigenvoice ...
Phong reflection model
For instance in face recognition those geometric constraints can be obtained using principal component analysis (PCA) on a ... For each light source in the scene, components i s {\displaystyle i_{\text{s}}} and i d {\displaystyle i_{\text{d}}} are ... Model-Based Illumination Correction for Face Images in Uncontrolled Scenarios. Lecture Notes in Computer Science. Vol. 5702. pp ... defined as the intensities (often as RGB values) of the specular and diffuse components of the light sources, respectively. A ...
Kernel Fisher discriminant analysis
These include: Face recognition and detection Hand-written digit recognition Palmprint recognition Classification of malignant ... for the Higgs Boson at CERN Factor analysis Kernel principal component analysis Kernel trick Linear discriminant analysis Mika ... "Face recognition using kernel-based Fisher discriminant analysis". IEEE International Conference on Automatic Face and Gesture ... Handwriting Recognition using Kernel Discriminant Analysis - C# code that demonstrates handwritten digit recognition using KFD ...
Linear discriminant analysis
In computerised face recognition, each face is represented by a large number of pixel values. Linear discriminant analysis is ... LDA is also closely related to principal component analysis (PCA) and factor analysis in that they both look for linear ... while those obtained using the related principal component analysis are called eigenfaces. In marketing, discriminant analysis ... Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization ...
Eigenvalues and eigenvectors
... this is an example of principal component analysis. They are very useful for expressing any face image as a linear combination ... for example in stability analysis, vibration analysis, atomic orbitals, facial recognition, and matrix diagonalization. In ... principal component analysis can be used as a method of factor analysis in structural equation modeling. Eigenvalue problems ... Principal component analysis is used as a means of dimensionality reduction in the study of large data sets, such as those ...
Multilinear principal component analysis
... face recognition - TensorFaces, (ECCV 2002, CVPR 2003, etc.) and computer graphics - TensorTextures (Siggraph 2004). ... Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA). MPCA is ... such as multilinear principal component analysis (MPCA), or multilinear independent component analysis (MICA), etc. The origin ... P. M. Kroonenberg and J. de Leeuw, Principal component analysis of three-mode data by means of alternating least squares ...
Reverse image search
Arista-DS only performs duplicate search algorithms such as principal component analysis on global image features to lower ... category recognition features, face recognition features, color features and duplicate detection features. Amazon.com disclosed ... eBay uses a ResNet-50 network for category recognition, image hashes are stored in Google Bigtable; Apache Spark jobs are ... Josh Constine (6 January 2014). "Pinterest Acquires Image Recognition And Visual Search Startup VisualGraph". TechCrunch. AOL. ...
Gait analysis
Rating Scale Gait deviations Multilinear principal component analysis Multilinear subspace learning Pattern recognition ... even if their faces are obscured. G. K. Chesterton premised one of his Father Brown mysteries, "The Queer Feet", on gait ... Gait Analysis. Authors: David F. Levine, Jim Richards, and Michael Whittle. Observational Gait Analysis. Author: Los Amigos ... This method has been aided by the development of analysis software that greatly simplifies the analysis process and allows for ...
Face space
Accordingly, principal component analysis is used to derive certain dimensions or 'eigenfaces' from sample faces, which can be ... Lewis, M. (2004). Face‐space‐R: Towards a unified account of face recognition. Visual Cognition, 11(1), 29-69. Lee, K., Byatt, ... In the norm-based model, the encoding of faces is relative to a central face at the origin: a 'norm face'. Faces are arranged ... faces found further away. Thus, since distinctive faces are located as more distant from other faces in the face-space (low ...
Facial motion capture
"Face recognition by elastic bunch graph matching", Computer Analysis of Images and Patterns, Lecture Notes in Computer Science ... principal component analysis, eigen tracking, deformable surface models and other techniques to track the desired facial ... The technology for markerless face tracking is related to that in a Facial recognition system, since a facial recognition ... Government's 2002 Facial Recognition Vendor Test (FRVT). On the other hand, some recognition systems do not explicitly track ...
Structured sparsity regularization
Structured sparse principal component analysis. In Proc. AISTATS, 2009. Rosasco, Lorenzo; Poggio, Tomaso (Fall 2015). "Chapter ... Examples of uses of structured sparsity methods include face recognition, magnetic resonance image (MRI) processing, socio- ... 2012). "Robust and Practical Face Recognition via Structured Sparsity". In Andrew Fitzgibbon; Svetlana Lazebnik; Pietro Perona ... potentially yielding significant reductions in MR scanning time Robust face recognition in the presence of misalignment, ...
Face hallucination
This method sees the solution as a transformation between different styles of image and uses a principal component analysis ( ... It can be applied in facial recognition systems for identifying faces faster and more effectively. Due to the potential ... these principal components can be inferred from the principal components of the low-resolution face by mapping between the high ... For high-resolution face images, PCA can compact this correlated information onto a small number of principal components. Then ...
Ensemble learning
Some different ensemble learning approaches based on artificial neural networks, kernel principal component analysis (KPCA), ... Face recognition, which recently has become one of the most popular research areas of pattern recognition, copes with ... "Hierarchical ensemble of Gabor Fisher classifier for face recognition". 7th International Conference on Automatic Face and ... "Patch-Based Gabor Fisher Classifier for Face Recognition". 18th International Conference on Pattern Recognition (ICPR'06). Vol ...
Mlpy
Fisher discriminant analysis (FDA), Spectral Regression Discriminant Analysis (SRDA), (kernel) Principal component analysis ( ... IEEE International Conference on Automatic Face & Gesture Recognition and Workshops 2011. Megies, T. et al (2011). ObsPy - What ... Pattern Recognition, 2010. Santana R. (2011) R. Santana. Estimation of distribution algorithms: from available implementations ... Methods for feature list analysis (for example the Canberra stability indicator), data resampling and error evaluation are ...
Condensation algorithm
... and through principal component analysis (PCA) on the deforming object. Isard and Blake model the object dynamics p ( x t , x t ... "Face recognition from video: A CONDENSATION approach". Proceedings of Fifth IEEE International Conference on Automatic Face ... The condensation algorithm has also been used for face recognition in a video sequence. An implementation of the condensation ... Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition. pp. 16-21. CiteSeerX 10.1.1.154.1402 ...
Protein domain
They can also be suggested by sampling in extensive molecular dynamics trajectories and principal component analysis, or they ... Problems occur when faced with domains that are discontinuous or highly associated. The fact that there is no standard ... Protein domain dynamics play a key role in a multitude of molecular recognition and signaling processes. Protein domains, ... December 2007). "Genetic analysis of attractin homologs". Genesis. 45 (12): 744-56. doi:10.1002/dvg.20351. PMID 18064672. S2CID ...
Five stages of grief
... based on thousands of subjects over two decades and concludes that a natural psychological resilience is a principal component ... People facing less serious trauma can bargain or seek compromise. Examples include the terminally ill person who "negotiates ... Brent MR (1981). An Attributional Analysis of Kübler-Ross' Model of Dying (Master's thesis). Harvard University. OCLC 77003423 ... the individual despairs at the recognition of their mortality. In this state, the individual may become silent, refuse visitors ...
Histogram of oriented gradients
PCA-SIFT descriptors are similar to SIFT descriptors, but differ in that principal component analysis is applied to the ... "Method of and apparatus for pattern recognition". "Orientation Histograms for Hand Gesture Recognition". "Histograms of ... method uses HOG descriptors in combination with the cascading classifiers algorithm normally applied with great success to face ... The HOG descriptor is then the concatenated vector of the components of the normalized cell histograms from all of the block ...
Curse of dimensionality
Tour Linear least squares Model order reduction Multilinear PCA Multilinear subspace learning Principal component analysis ... A unified embedding for face recognition and clustering" (PDF). 2015 IEEE Conference on Computer Vision and Pattern Recognition ... McLachlan, G. J. (2004). Discriminant Analysis and Statistical Pattern Recognition. Wiley Interscience. ISBN 978-0-471-69115-0 ... In time series analysis, where the data are inherently high-dimensional, distance functions also work reliably as long as the ...
Michael J. Black
De la Torre, F.; Black, M.J. (2001). "Robust principal component analysis for computer vision". Int. Conf. on Computer Vision ( ... Face, and Body from a Single Image". IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 36. pp. 10975- ... and principal-component analysis (PCA). The robust formulation was hand crafted and used small spatial neighborhoods. The work ... which has become an important component of self-supervised training of neural networks for problems like facial analysis. ...
Autoencoder
The autoencoder was first proposed as a nonlinear generalization of principal components analysis (PCA) by Kramer. The ... When facing anomalies, the model should worsen its reconstruction performance. In most cases, only data with normal instances ... Nair, Vinod; Hinton, Geoffrey E. (2009). "3D Object Recognition with Deep Belief Nets". Proceedings of the 22nd International ... of the code yielded a smaller reconstruction error compared to the first 30 components of a principal component analysis (PCA ...
K-nearest neighbors algorithm
Feature extraction and dimension reduction can be combined in one step using principal component analysis (PCA), linear ... An example of a typical computer vision computation pipeline for face recognition using k-NN including feature extraction and ... Popular algorithms are neighbourhood components analysis and large margin nearest neighbor. Supervised metric learning ... Haar face detection Mean-shift tracking analysis PCA or Fisher LDA projection into feature space, followed by k-NN ...
Biological motion perception
First, Principal component analysis was done on full body 2d walkers and point light walkers. The analysis found that dominant ... There are many brain areas involved in this process, some similar to those used to perceive faces. While humans complete this ... Humans do not detect biological figures in static environments and motion is an inherent aspect in upright figure recognition. ... However, to what extent each of the components play is contrasted upon the models. Research in this area seeks to identify the ...
History of artificial neural networks
Nanodevices for very large scale principal components analyses and convolution may create a new class of neural computing ... won three competitions in connected handwriting recognition at the 2009 International Conference on Document Analysis and ... Behnke (2003) relied only on the sign of the gradient (Rprop) on problems such as image reconstruction and face localization. ... Ciresan and colleagues won pattern recognition contests, including the IJCNN 2011 Traffic Sign Recognition Competition, the ...
One-shot learning (computer vision)
This dimensionality is reduced using principal component analysis, and A {\displaystyle A} , the appearance parameter, is then ... Biederman, I. (1987). "Recognition-by-Components: a theory of human understanding" (PDF). Psychological Review. 94 (2): 115-147 ... To obtain shape and appearance priors, three categories (spotted cats, faces, and airplanes) are learned using maximum ... One-shot learning differs from single object recognition and standard category recognition algorithms in its emphasis on ...
Diffusion map
This is a major difference with methods such as principal component analysis, where correlations between all data points are ... Applications based on diffusion maps include face recognition, spectral clustering, low dimensional representation of images, ... Different from linear dimensionality reduction methods such as principal component analysis (PCA), diffusion maps are part of ... "Fast high dimensional vector multiplication face recognition" (PDF). Proceedings of the IEEE International Conference on ...