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The current paper suggests an initial segmentation system, detection and grouping of visual information based on faces to facilitate the use, the handling and validation of the VIDTIMIT database. In order to implement this basis, two methods of pretreatment for each face are used. Then faces are detected with the Viola and Jones algorithm based on weak type descriptors Haar and grouped by new faces...
One of the major challenges in face recognition is that related to the differences in orientation or pose, the variations of illumination, the facial expressions, the occlusions and aging. In this paper, we propose an efficient method for face recognition in an uncontrolled environment where we fuse Gabor wavelets and Local Binary Patterns (LBP) in the feature extraction phase. Then, we apply the...
An efficient face recognition system should recognize faces in different views and poses. The efficiency of a human face recognition system depends on the capability of face recognition in presence of changes in the appearance of face due to expression, pose and illumination. A novel algorithm which utilizes the combination of texture and depth information based on Modular PCA to overcome the problem...
To solve the challenging problem of face recognition under varying illumination conditions, we propose in this paper a novel LBP operator which we refer to as Local Binary Patterns with Circle Threshold (CT-LBP) operator. The CT-LBP operator can keep more discriminating information than the original LBP operator without losing the simplicity and effectivity of the original LBP operator. Extensive...
An efficient illumination invariant face recognition method based on two-stage two dimensional linear discriminant analysis (2S2DLDA) is presented in this paper. The proposed method uses a reflectance-illumination model (RI-Model) based on maximum filter to obtain illumination invariants of an image. Various combinations of two dimensional feature extraction techniques (PCA, 2DPCA family and 2DLDA...
The development of accurate and robust palmprint recognition algorithm is a critical issue in automatic palmprint recognition system. In this paper, we propose a palmprint recognition method based on a two-phase test sample sparse representation. In the first phase, a test sample is represented as a linear combination of all the training samples and m "nearest neighbors" are selected based...
Biometric devices provide secure mechanism towards gaining access. One of the Biometric features is Face and the system implemented is Face Recognition system. The Classical Face Recognition System is implemented with Principal Component Analysis and is successful. PCA is a linear method of extracting the features in a lower dimension space and is severely affected by the Pose and surrounding illumination...
This paper presents a novel scheme for feature extraction for face recognition by fusing local and global discriminant features. The facial changes due to variations of pose, illumination, expression, etc. are often appeared only some regions of the whole face image. Therefore, global features extracted from the whole image fail to cope with these variations. To address these problems, face images...
Illumination compensation has proven to be crucial in face detection and face recognition. Several methods for illumination compensation have been developed and tested on the face recognition task using international available face databases. Among the methods with best results are the Discrete Cosine Transform (DCT), Local Normalization (LN) and Self-Quotient Image (SQI). Most of these methods have...
With illumination varying condition, face features gotten from image is distorted nonlinearly by variant lighting intensity and direction, so face recognition becomes very difficult. According the "common assumption" that illumination vary slowly and the face intrinsic feature (including 3D surface and reflectance) vary rapidly in local area, we can consider that high frequency features...
One of the challenges in face recognition system is to deal with inhomogeneous intensity problem that occur with different lighting conditions. In this paper, comparisons are made on several pre-processing methods i.e. histogram equalization, local binary pattern, wavelet transform and multiscale retinex. First, the input image is pre-processed with the illumination correction method before the classification...
Facial images are affected by multiple factors including facial geometries, expressions, viewpoints and illuminations. We apply multi-linear algebra to separate these factors and extract the people factor used for face recognition. Compared to standard PCA and its variants, the method allows for bigger changes in viewpoints and illuminations. Our method is based on the method, adding color information,...
Illumination variations that might occur on face images degrade the performance of face recognition systems. In this paper, we propose a novel method of illumination normalization based on retina modeling by combining two adaptive nonlinear functions and a Difference of Gaussians filter. The proposed algorithm is evaluated on the Yale B database and the Feret illumination database using two face recognition...
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...
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