Proceedings ArticleDOI
Preprocessing using SVD towards illumination invariant face recognition
K. Punnam Chandar,Mahesh Chandra,M. Raman Kumar,B. Swarnalatha +3 more
- pp 051-056
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TLDR
A preprocessing scheme using Singular Value Decomposition and Histogram Equalization to enhance and facilitate illumination invariant face recognition and is compared with PCA, KPCA without any preprocessing.Abstract:
Uncontrolled lighting Conditions poses obstacle to face recognition. To deal with this problem, this paper proposes a preprocessing scheme using Singular Value Decomposition and Histogram Equalization to enhance and facilitate illumination invariant face recognition. The proposed method first generates synthetic image using Histogram equalization. Original and synthetic images are singular value decomposed; from the estimates of singular values enhanced image is reconstructed. Enhanced image is discrete wavelet decomposed (Haar & Db4) in to different frequency sub bands (LL, LH, HL, HH). The LL sub band is the best approximation of original image with lower-dimensional space and is used as biometric template. Pose Invariant Feature vectors are extracted from this template using Kernel Principal Component Analysis (KPCA). To show the performance, the proposed method is tested on YaleB, ORL benchmarking Databases. The results obtained show the impact of the method and is compared with PCA, KPCA without any preprocessing.read more
Citations
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Journal ArticleDOI
Local SVD based NIR face retrieval
TL;DR: The experimental results confirm the superiority of using S sub-band of SVD in terms of performance of the local descriptors over NIR face databases.
Book ChapterDOI
Face Recognition Using PCA and Bit-Plane Slicing
TL;DR: In the proposed frame work image is decomposed with the help of bit plane slicing, the feature have been extracted from the principle component analysis (PCA) and the design of PCA on bit plane reduces computation complexity and also reduces time.
Proceedings ArticleDOI
A SVD Based Pattern Matching Approach for Color Image Retrieval
TL;DR: SVD technique followed by LBP (Local Binary Pattern) algorithm has been applied to the images in RGB color space and CBIR performed upon these feature vectors yielded better results and values of precision, recall and f-score was found to be 56.08, 84.13 and 67.3 respectively.
Dissertation
Multi-objective feature extraction and ensembles of classifiers for invariant image identification
TL;DR: An effective feature extraction approach using Trace transform and ensembles of classifiers for invariant image identification and Pareto optimality principles to select the best functionals used in Trace transform is proposed.
Book ChapterDOI
Evaluating Effectiveness of Color Information for Face Image Retrieval and Classification Using SVD Feature
TL;DR: SVD (Singular Value Decomposition) is applied to individual component of a color space followed by LBP to get the final feature vector, which enables in extracting and identifying more prominent features and the accuracy could be increased.
References
More filters
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
Active appearance models
Abstract: We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
Journal ArticleDOI
From few to many: illumination cone models for face recognition under variable lighting and pose
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.
Book ChapterDOI
Active Appearance Models
TL;DR: A novel method of interpreting images using an Active Appearance Model (AAM), a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example.