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Face recognition in real scenarios is mainly affected by illumination variation and occlusion, and therefore in order to develop a robust face recognition system these issues should be handled simultaneously. To this aim, the steps involved in the presented framework are (i) computationally simple and efficient preprocessing chain that eliminates major effects of illumination variation and noise while...
Recently, deep features extracted from Convolutional Neural Networks (CNNs) have been widely adopted in various applications, such as face recognition. Compared with the handcrafted descriptors, deep features have more powerful representation ability which can lead to better performance. Effective feature representations play an important role in ear recognition. While deep features have not been...
In this paper we propose a new powerful face recognition method to increase the performance of face recognition algorithms. In our idea we integrate two dissimilarity measures namely City-block and Mahalanobis Cosine distance. The experiments are performed on the ORL database and YALE database. The results indicate the interest of the proposed technique compared to others methods of literature.
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...
In recent years considerable progress has been made in the area of face recognition. Through the work of computer science engineers, computers can now outperform humans in many face recognition tasks, particularly those in which large databases of faces must be searched. A system with the ability to detect and recognize faces has many potential applications including crowd and airport surveillance,...
Face recognition at a distance is still a challenging problem due to the low resolution face images resulting from the remote distance. To motivate researches on the problem and make up for the shortage of existing databases, we introduce MDCI database in this paper. The database contains 677 videos and 9734 pictures from 155 subjects captured by five different cameras, at four kinds of distances...
In this paper, we proposed a template based face recognition approach. Here we compared our approach with the holistic feature based approach Principal Component Analysis (PCA). PCA is a statistical feature based approach works on Eigen space. PCA is a simple approach for face recognition of only frontal faces and proposed system is based on grey level template matching. To know the greatness of proposed...
Automated recognition of facial expression has attracted significant attention in recent years due to its potential applicability in security and surveillance, human computer interaction, social robotics, and animation. This paper presents a new facial expression recognition method that utilizes bit plane specific local image description in a weighted score level fusion. The motivation is to utilize...
The biggest bottleneck of face recognition is the insufficient training samples of face images. In reality, face possess varying nature of expressions, illumination, poses, etc. There is a constrained of insufficient training samples due to number of reasons which reduces the accuracy of face recognition techniques. A method to generate various symmetrical face images by exploiting the axis-symmetrical...
As modern technology evolves, the use of face recognition system is scattering in different sectors of commercial markets rather than in security purposes only. Various approaches are introduced for face recognition system, among them principal component analysis is one of the simplest and efficient method. To improve the performance of face recognition, choosing a threshold value and minimum number...
Over the past decade, a considerable amount of literature has been published on face recognition. Since recognition of frontal face images under controlled settings has become easy to achieve, a number of recent studies have emphasized the importance of robustness to variations in pose and illumination. So in this paper, we undertake the task of recognizing face images taken under drastic lighting...
Discriminant analysis is an important technique for face recognition because it can extract discriminative features to classify different persons. However, most existing discriminant analysis methods fail to work for single-sample face recognition (SSFR) because there is only a single training sample per person such that the within-class variation of this person cannot be estimated in such scenario...
Illumination variation is one of the well-known and challenging problems known for face recognition. A lot of studies have been explored to reduce the effect caused by varying illumination. We have analysed the latest state of art technique and have divided into two categories. One based on Singular Value Decomposition (SVD), Resonance and Local Binary Pattern (LBP) techniques. And the second based...
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...
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,...
A visible image–based face recognition system can be seriously degraded in real-life environments by various factors including illumination changes, expression changes, occlusion, and disguise. In this paper, a novel feature descriptor for robust face recognition, Eigen Directional Bit-Plane (EDBP), is introduced to address these issues. It is observed that Local Binary Pattern (LBP) can be decomposed...
In this paper the effectiveness of different classification techniques is evaluated on the performance of face recognition algorithms. Gabor wavelet and its fusion with local binary pattern (LBP) are utilized as feature extractors. Dimensionality reduction approaches, principal component analysis (PCA) and Fisher's linear discriminant (FLD), are employed to reduce the size of feature vector. The performance...
In recent years, significant achievements have been achieved in the field of face recognition. Face recognition are special pattern recognition which are used in banking for identity approving and the entrance of controlled areas, the places where the security control impending to airports, to control machines, to follow-up of persons. In this study, face recognition applications on the Yale face...
A face recognition algorithm, based on wavelet stretching transformation pre-processing, has been applied to principle component analysis (PCA). PCA is one of the most important methods for feature extraction and feature recognition. The high recognition rate is based on the hypothesis that the relationship between different dimensions is linear. The gray scale of images is vulnerable to the effect...
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...
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