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The last research efforts made in the face recognition community have been focusing in improving the robustness of systems under different variability conditions like change of pose, expression, illumination, low resolution and occlusions. Occlusions are also a manner of evading identification, which is commonly used when committing crimes or thefts. In this work we propose an approach based on the...
Existing face recognition methods suffer from efficiency problems and heavily rely on proper features extraction. In this paper, we propose an efficient face classification method which aims to reduce sensitivity to facial variations and occlusions, meanwhile complete tasks efficiently. In contrast with most energy minimizing based recognition methods, proposed algorithm is cast as a simple classification...
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In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the ability to handle non-stationary outliers, i.e. outliers which have time varying support. We validate our approach with empirical results showing the superiority of the proposed method over competing approaches in synthetic...
In this work we present a general framework for robust error estimation in face recognition. The proposed formulation allows the simultaneous use of various loss functions for modeling the residual in face images, which usually follows non-standard distributions, depending on the image capturing conditions. Our method extends the current vast literature offering flexibility in the selection of the...
We address the problem of makeup face recognition. Our main idea is to incorporate different levels of features into a joint optimization framework. Specifically, we combine both mid-level (e.g. attributes) and low-level features to obtain a new representation for a better matching between makeup and non-makeup faces. Previous studies have discovered the influence of cosmetics on face recognition,...
This paper focuses on algorithmic approaches to solve the robust face recognition problem where the test face image can be corrupted. The standard approach is to formulate the problem as a sparse recovery problem and solve it using ℓ1-minimization. As an alternative, the approximate message passing (AMP) algorithm had been tested but resulted in pessimistic results. Our contribution is to successfully...
This paper presents a novel method for robust face recognition, termed non-negative sparse low-rank representation classification (NSLRRC). NSLRRC seeks a sparse, low-rank and non-negative matrix over all training samples. Sparse constraint makes representation vector discriminative, while low-rank matrix will expose the global structures of data. Meanwhile, non-negative representation vectors guarantee...
At the last decades, face analysis remains a challenging research topic in the computer vision area. Beyond the visible band, infrared images had shown several advantages for face detection and recognition. From the proposed approaches for analyzing these images, the local analysis is recognized by its feasibility to overcome typical undesirable conditions such as noise, illumination, and affine transformations...
In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local...
Participation of class-wise noisy patterns may mislead the selection process of relevant patterns for subspace projection. And modelling between-class scatter for each class using the patterns that are nearer to the corresponding class decision boundary may improve the quality of feature generation. In this manuscript, a novel dimensionality reduction method, named Maximum Class Boundary Criterion...
The nearest subspace classifier (NSC) assumes that the samples of every class lie on a separate subspace and it is possible to classify a test sample by computing the distance between the test sample and the subspaces. The sparse representation based classification (SRC) generalizes the NSC - it assumes that the samples of any class can lie on a union of subspaces. By calculating the distance between...
Face recognition provides a challenging issue in the domain of analyzing images. In this paper a novel approach for face recognition using hybrid SIFT-SVM is proposed. The current database is divided into two parts; training and testing database. The SIFT feature will be created for each training images and the keypoints are computed; then the SVM is applied for the matching process for test images...
Numerous approaches are developed for the face recognition and its related applications. These approaches are solely dependent upon the kind of feature vectors used for extracting the different characteristics and structural contents from the image. The ability of extracting features from the image is either local or global. In this paper, we present a fusion of local features i.e. Local Binary Pattern...
Robust sparse representation has been applied to tackle some challenging problems in face recognition. In this paper, we propose a new method called occlusion pattern based sparse representation classification (OPSRC). First we find the contiguous occlusion area in the query image to create an occlusion pattern. Then, we add the occlusion pattern to all face images in the face image dictionary, resulting...
Large age range is a serious obstacle for automatic face recognition. Although many promising results have been reported, it still remains a challenging problem due to significant intra-class variations caused by the aging process. In this paper, we mainly focus on finding an expressive age-invariant feature such that it is robust to intra-personal variance and discriminative to different subjects...
This paper explores the viability of Hartley Transforms as an alternative to Fourier Transforms for Face Recognition. The paper provides a brief introduction to Hartley Transform, which is a reasonable alternate to Fourier Transform due to its similarities in the choice of basis function. Correlation filter is a pattern recognition tool that is efficient and robust. This includes extraction of features...
Attendance recording of a student in an academic organization plays a vital role in judging students performance. As manual labor involved in this process is time consuming, an automated Attendance Management System (AMS) based on face detection and face recognition techniques is proposed in this paper. The system employs modified Viola-Jones algorithm for face detection, and alignment-free partial...
Face alignment is an important pre-processing step for face analysis systems. Especially, the performance of face recognition systems can be improved by using aligned face images. In this work, we used matrix decomposition based and SIFT features based methods in face alignment. We performed recognition experiments by using raw versus aligned images with an image set based classification method. We...
Face recognition is one of the most popular biometric today. However, there are still challenges in the development of a robust, real-time face recognition system. Several challenges can be listed as poor illumination, rotations of the face and deformations on the face caused by factors like aging. The most frequent deformations on the face are due to facial expressions that indicate the emotional...
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