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The linear discriminant analysis (LDA) is one of the most efficient supervised dimensionality reduction technique widely used in face recognition. This paper proposed a new weighted LDA to improve the performance of the discriminant analysis. Confusable pair of classes is considered as the primary goal in our objective function. The proposed technique not only improves the minimization of the within-class...
Like fingerprint, human face can be applied as a security system because it has almost the same characteristics as that of fingerprint, in terms of the uniqueness and non transferable. Therefore, in this paper, we design and simulate fast human face recognition for the security system. It is realized by implementing the compact features of face image as data dimensional reduction and the shifting-mean...
Variations in pose, expression, aging and disguise are considered as basic challenges in face recognition systems and several standard databases have been built to address these challenges. Twins face recognition, on the other hand, has not yet been studied because there is no database which includes face images of twins. In this paper we present a new unconstrained face database which contains 3,804...
In this paper, we propose a new appearance based approach for palmprint recognition, which combines Kernel Spectral Regression Discriminant Analysis (KSRDA) method and HOG representation. KSRDA is the kernel version of SRDA which has lower computation complexity than Linear Discriminant Analysis (LDA). Meanwhile, HOG representation isn't sensitive to changes of illumination, and has the robustness...
The variation of facial appearance due to the illumination degrades face recognition systems considerably, which is well known as one of the bottlenecks in face recognition. However, the variations of each subject which are due to the changes of illumination are extremely similar to each other. We offline collect many face classes each of which has many images under different lighting conditions,...
In this paper, we exploit the multi-modal face recognition capability by a comparative study on 6 fusion methods in the score level, which can be divided into 2 kinds: (1) simple fusion without data training, such as Sum, Product, Max and Min; (2) complex fusion including a predefined data training section, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Our experiments...
A novel face recognition method is proposed in this paper to alleviate the "Small Sample Size" problem of the conventional Linear Discriminant Analysis (LDA). This method is based on the feature extraction of global odd and even face image representation, and a dimension reduction process via Symmetrical 2D Partial Least Square Analysis (2DPLS) by two sizes. The low-dimensional features...
To date, most facial expression analysis has been based on visible and posed expression databases. Visible images, however, are easily affected by illumination variations, while posed expressions differ in appearance and timing from natural ones. In this paper, we propose and establish a natural visible and infrared facial expression database, which contains both spontaneous and posed expressions...
In this paper, an efficient local appearance feature extraction method based steerable pyramid (S-P) is proposed for face recognition. Local information is extracted from S-P sub-bands using block-based statistics. The underlying statistics allow us to reduce the required amount of data to be stored. The obtained local features are combined at the feature and decision level to enhance face recognition...
In this paper, we introduce a novel face representation method for face recognition, called Local Line Binary Pattern (LLBP), which is motivated from Local Binary Pattern (LBP) due to it summarizes the local spacial structure of an image by thresholding the local window with binary weight and introduce the decimal number as a texture presentation. Moreover it consumes less computational cost. The...
In this paper, we study face recognition using principal component analysis (PCA) and linear discriminant analysis (LDA) under illumination variations. A modified census transform (MCT) is applied as preprocessing step to compensate illumination variations, and then PCA and LDA are employed to find lower-dimensional subspaces for face recognition. Distances between training and testing images are...
In order for robots to be able to manipulate the proper objects, robots firstly need visual ability to precisely recognize and identify objects. One of the most basic problems with robot vision is that environments can change under various weather conditions (various illuminations). Furthermore, each object's category consists of many objects with various poses. In order to obtain the best performance...
The paper introduces a feature extraction technique for face recognition called the phase-based Gabor Fisher classifier (PBGFC). The PBGFC method constructs an augmented feature vector which encompasses Gabor-phase information derived from a novel representation of face images - the oriented Gabor phase congruency image (OGPCI) - and then applies linear discriminant analysis to the augmented feature...
In many various applications facial images are dramatically changed especially by lighting variations, so that facial appearance changes caused serious performance degradation in face recognition. In this paper we describe a method to address illumination removal for face recognition using Empirical Mode Decomposition (EMD) to decompose subimages of Dual-Tree Complex Wavelet Transform (DT-CWT) into...
In this paper, chromatic information is integrated with an Adaboost learner to address non linearities in face patterns and illumination variations in training databases for face recognition (FR). An LDA based learner is boosted and the integrated framework is tested on a large database of images having severe pose and illumination variations. The increased dimensionality of color induces a small...
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