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Single face-image comparisons are extremely challenging, particularly in the context of pose, expression variations and scene illumination changes. Most of the existing schemes are sub-space learning based, where dominant eigen-directions are determined from the covariance matrix computed over the entire face space. In this paper we propose a simple hashing method based on the relative magnitudes...
In this work, our main objective is to enhance the performance of the optical correlator which is VunderLaugt (VLC) correlator method for face recognition applications. We propose a pre-processing phase to improve robustness of VLC correlator for face recognition applications. Our technical results prove that this method is remarkably efficient to enhance the performances of a VLC correlator. We have...
This paper presents a novel quantized gradient based local feature descriptor, named Local Quantized Gradient Direction (LQGD) descriptor and the subsequent Partitioned Gradient Histogram, for facial image representation. The 8 bit LQGD descriptor accommodates eight levels quantized gradient magnitude and direction information from the horizontal and vertical gradients at local facial image pixels...
Transgender face recognition is gaining increasing attention in the face recognition community because of its potential in real life applications. Despite extensive progress in traditional face recognition domain, it is very challenging to recognize faces under transgender setting. The gender transformation results in significant face variations, both in shape and texture gradually over time. This...
Face recognition (FR) via regression analysis based classification has been widely applied in the past several years. In the existing regression methods, the testing image is represented as a linear combination of the training samples and the error image is converted into vector which is characterized by l1-norm or l2-norm. Therefore the two-dimensional structure of the error image is neglected in...
Systems for still-to-video face recognition (FR) are typically used to detect target individuals in watch-list screening applications. These surveillance applications are challenging because the appearance of faces change according to capture conditions, and very few reference stills are available a priori for enrollment. To improve performance, an adaptive appearance model tracker (AAMT) is proposed...
In this paper, we propose an efficient expression-invariant 3D face recognition algorithm to compute the minimum-distortion mapping between two 3D face by the Generalized MultiDimensional Scaling (GMDS). Both full and partial parts matching are computed for finding the least distortion embedding of one 3D face into another during GMDS. The problem of expression-invariant three-dimensional face recognition...
Many research works have been done in face recognition during the last years that indicates the importance of face recognition systems in many applications including identity authentication. In this paper we propose an approach for face recognition which is suitable for unconstrained image acquisition and has a low computational cost. Since in practical applications such as in smartphones, imaging...
In this paper we introduce a new dataset and pose invariant sampling method and describe the ensemble methods used for recognizing faces in 3D scenes, captured using commodity depth sensors. We use the 3D SIFT key point detector to take advantage of the similarities between faces, which leads to a set of points of interest based on the curvature of the face. For all key points, features are extracted...
Recognizing faces in presence of illuminations, pose, facial expression variations in controlled as well as uncontrolled environments remains one of the most challenging aspect. In this paper, we propose a novel recognition methodology which deals with challenges of face recognition to obtain robust and efficient recognition. The framework is based on extracting discriminant statistical features from...
This paper presents a novel facial expression recognition approach in the presence of partial occlusion using Gabor filters and gray-level co-occurrence matrix (GLCM). At first, we design an algorithm to extract the block Gabor feature statistics according to the spatial distribution of the face organ. Then, GLCM is firstly introduced into expression recognition field to make up for the deficiency...
We consider the problem of robust face recognition in which both the training and test samples might be corrupted because of disguise and occlusion. Performance of conventional subspace learning methods and recently proposed sparse representation based classification (SRC) might be degraded when corrupted training samples are provided. In addition, sparsity based approaches are time-consuming due...
Global standards for cattle recognition, registration and traceability are being developed. However missed or swapped cattle, false insurance claims and reallocation of cattle at slaughter houses are global problems throughout the world. Previous cattle recognition approaches have their own boundaries and they are not able to provide required level of security to cattle livestock. In this paper, an...
Local ternary pattern (LTP) is a noise-robust version of local binary pattern (LBP). They are both encoding for the differences between the intensity of the center pixel and its neighborhoods. In this paper, based on Webers law we propose two new local descriptors, named Weber binary pattern (WBP) and Weber ternary pattern (WTP), which utilize binary and ternary encoding separately for the evaluation...
In the field of automatic face recognition, transformations of facial features due to aging cause a problem. Due to small amounts of extracted features, the identity verification can be difficult. The feature-based methods that are present in the literature are still being developed, with unsatisfactory results caused by high rates of false matching. In this paper we present a new method of matching...
The Gabor filters are considered one of the best image representation approaches for face recognition (FR). Researchers have exploited various configurations of Gabor magnitude as well as Gabor phase responses and their modeling with other descriptors. In this paper, we propose a novel face representation approach; Local Gabor Rank Pattern (LGRP), which exploits ordinal ranking of Gabor response images...
In today's age of automation, face recognition is a vital component for authorization and security. It has received substantial attention from researchers in various fields of science such as biometrics and computer vision. In this paper, a face recognition system using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is analysed. A neural based algorithm is presented...
Recently, nuclear norm based matrix regression (NMR) for classification has been proposed to characterize the whole structure of the error image. However, NMR ignores both the label information and the group structure of training samples. This paper presents a novel yet effective coding scheme called locality-constrained group sparse coding regularized NMR (LGNMR) which not only overcomes these limitations...
Local binary pattern (LBP) is sensitive to noise. LBP projects local patch to eight-dimension vector by operating subtractions between pixel and its neighborhood. Two adjacent pixel values are generally very close, thus little noise can change their relative magnitude, leading to coding err. Using another projecting approach, random projection, as an alternate, we propose local binary pattern based...
The most effective and natural means for human beings is Facial expression that have the dexterity to communicate emotion and regulate inter-personal behaviour. We proposed a novel facial-expression analysis system design that focused on automatically recognize facial expressions and reducing the doubt and confusion between facial-expression classes. The information used in facial expression concentrates...
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