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In face recognition, the illumination variation problem in uncontrolled environments has gained some research activities. Although the quotient image based methods are reported to be a simple yet practical technique in face recognition, these methods could not satisfactorily maximize the ratios of between-class and within-class scatter and may not effectively be used for the illumination variation...
In this work we present an extension of the SIFT algorithm to color images. In the extrema detection stage, an energy level descriptor based on the color tensor of the image is computed and used to locate keypoints candidates. Then, in the description stage, the color gradient magnitude and orientation of the samples around the keypoint are used to compute an orientation histogram to create the keypoint...
In this research paper an extensive literature survey on different types of feature extraction techniques is reported. To provide an extensive survey, we not only categorize existing feature extraction techniques but also provide detailed descriptions of representative approaches within each category. These techniques are simply classified into four major categories, namely, feature based approach,...
Availability of a single training sample (STS) or degraded set (DS) of training and testing samples restricts the success of face recognition in real-world applications. We propose a unified framework for handling both these challenges simultaneously by using a data dictionary, which is a combination of training dictionary and intra-class variation dictionary. The training dictionary is assembled...
Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial...
In this paper, we investigate face recognition in unconstrained illumination conditions. A twofold contribution is proposed: First, three state of the art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are challenged against the IRIS-M3 multispectral face data base to evaluate their robustness...
This paper presents a new method to automatically locate pupils in images (even with low-resolution) containing human faces. In particular pupils are localized by a two steps procedure: at first self-similarity information is extracted by considering the appearance variability of local regions and then they are combined with an estimator of circular shapes based on a modified version of the Circular...
This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. We first develop a robust face tracking algorithm based on the local sparse appearance. This sparse representation model exploits both partial and spatial information of the face based on a covariance pooling method. Following in the face recognition stage, with the employment of a novel...
This paper proposes a technique for face feature extraction using sinusoidal projection. Essentially, the technique uses a projection matrix, which is formed by stacking vectors with sinusoidal values at different frequencies, to directly multiply with raw image matrix for weighted feature extraction. Orthogonality among vectors within the sinusoidal projection matrix is observed when the frequencies...
Robust high-dimensional data processing has witnessed an exciting development in recent years, as theoretical results have shown that it is possible using convex programming to optimize data fit to a low-rank component plus a sparse outlier component. This problem is also known as Robust PCA, and it has found application in many areas of computer vision. In image and video processing and face recognition,...
Phase eigen subspace based face recognition under varying lighting conditions is proposed. Universal subspace analysis is exploited in frequency domain and phase spectrum is extracted instead of using raw spatial data of face images. Improved results are obtained when simplified bi-directional associative memory neural network is used as classifier. The proposed scheme is experimented over two standard...
The development of a fully automatic facial expression recognition system is an open problem. Its implications are very important, with applications ranging from machine intelligence and interaction to psychology research. In order to obtain a viable system, it is necessary to get valid parameters to characterize the facial expression in an image or a video sequence. Several different techniques have...
Correction of uneven illumination in face images has been a task of intense research with its applicability being extensive. Most algorithms look to improve recognition accuracy failing to produce visually good results or viceversa. In this work, we present a data-driven illumination correction algorithm which simultaneously produces visually good results and improves recognition accuracy among faces...
This paper addresses the problem of face recognition under variation of illumination and poses with large rotation angles using edge information as Independent Component (ICs). The edge information is obtained by using Laplacian of Gaussian (LoG) and second order differential edge detection methods. Then pre-processing is done by using Principle Component analysis (PCA) before applying the Independent...
We consider the problem of matching highly non-ideal ocular images where the iris information cannot be reliably used. Such images are characterized by non-uniform illumination, motion and de-focus blur, off-axis gaze, and non-linear deformations. To handle these variations, a single feature extraction and matching scheme is not sufficient. Therefore, we propose an information fusion framework where...
Local Binary Patterns (LBPs) and Covariance Matrices (CovMs) are two popular kinds of texture descriptors. However, local correlation brought by LBPs and global correlation brought by CovMs could not be directly combined to achieve enhanced discriminative power. This paper develops a powerful descriptor, named COV-LBP. Firstly, we propose a variant of LBPs on Euclidean space, named the LBP Difference...
The notion of metric is fundamental for the study of pattern recognition and vector 2-norm ║·║ 2 is one of the most widely used metric, i.e., Euclidean distance. However, there is often the case that the inputs are matrices, e.g., 2D images in face recognition. Since a matrix can take more structure information than its vectorization, it is highly preferable to adopt the matrix representation of the...
Palmprint recognition has attracted much attention in recent years. Many algorithms based texture coding achieve high accuracy. However they are still sensitive to local unsteady region introduced by variations of hand pose and other conditions. In this paper we proposed a novel feature extraction algorithm, namely binary contrast context vector (BCCV), to represent multiple contrast distribution...
Linear Regression Classifier (LRC) is state-of-the-art face recognition method that represent a probe image as a linear combination of class specific models. However, this method views the image as a point in a feature space, and thus LRC cannot accommodate severe luminance alterations. Histogram-based features, such as Multiscale Local Phase Quantisation histogram (MLPQH) have gained reputation as...
In the context of vision-based topological navigation, detecting loop closures requires to compare the robot's current camera image to a large number of images stored in the map. For efficient image comparisons, we apply distance functions to global image-descriptors, i.e. low-dimensional descriptors derived from the entire panoramic images. To identify promising combinations of descriptors and distance...
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