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Face recognition has been receiving continuous academic and commercial attention for the last decades. In this paper, we construct two face recognition systems adopting SVM and Adaboost as the classifiers with fast PCA for facial feature representation. The detailed discussions about algorithm realization are given. Comparison between the two systems and analysis of them are provided through several...
This paper proposes a method of facial expression recognition based on Zernike moments and the minimum classification error (MCE) based hidden Markov model (HMM). In the feature extraction of face, the method of Zernike moments feature extraction based on local feature regions is adopted. First, eyes and mouths are segmented from the facial expression image and Zernike moments feature vectors of eyes...
Facial expression recognition in complex environment is one of the difficult tasks of visual recognition in recent years. This paper introduces the visual saliency mechanism and we design automatic searching of the face region in the image. Using the narrow band C-V model to evolve curve, the proposed scheme can obtain the accurate face region. Meanwhile, the SVM will be trained by standard database...
A principal components analysis (PCA) algorithm is one of the most important algorithms that has been used for doing many tasks; for example, data dimension reduction, data compression such as image compression, pattern recognition such as face detection and recognition, and many other things. An improved principal components analysis (IPCA) algorithm is similar to the PCA algorithm except that it...
This research proposes a novel Bayesian sparse representation (BSR) method along with extracting facial parameters of SIFT to create sparse dictionaries, which are invariant to rotation, scale, and shift. By using K-means and information theory, a new dictionary called extended dictionary is developed. Compared with conventional orthogonal matching pursuit (OMP) algorithm, the proposed system that...
Gait recognition with a single sample per person (SSPP) is a challenging problem but has so far drawn little research attention. Inspired by similar research in face recognition, we propose to utilize the intra-class variation information learned from an additional generic training set with multiple samples per person to improve the representation of the query sample. We learn a sparse variation dictionary...
When analysing human activities using data mining or machine learning techniques, it can be useful to infer properties such as the gender or age of the people involved. This paper focuses on the sub-problem of gender recognition, which has been studied extensively in the literature, with two main problems remaining unsolved: how to improve the accuracy on real-world face images, and how to generalise...
Face detection and face attribute recognition, as hot topics in the field of computer vision, have been well studied. However, over the years, face detection and attribute recognition are regarded as different tasks and designed separately, which ignores the fact that they both classify samples based on the knowledge of skin color, face outline and face components etc. In this paper, we describes...
In this paper, we propose a new approach for recognition of low-resolution face images by using sparse coding of local features. The proposed algorithm extracts Gabor features from a low-resolution gallery image and a query image at different scales and orientations, then projects the features separately into a new low-dimensional feature space using sparse coding that preserves the sparse structure...
We propose a bilateral hemiface feature representation learning via convolutional neural networks (CNNs) for pose robust facial expression recognition. The proposed method considers two characteristics of facial expressions. First, features from local patches are more robust to pose variations. Second, human faces are bilaterally symmetrical on left and right hemifaces. To incorporate those characteristics,...
In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show...
In consideration of the problem that the existing face recognition methods cannot handle the face recognition under unsatisfactory situations, such as shadows, occlusions, stains, which cause low recognition rate. Therefore, an algorithm based on discriminative low-rank matrix recovery with sparse constraint (DLRRSC) is proposed. First, discriminative low-rank matrix recovery is used to correct the...
Recognizing textual entailment is typically considered as a binary decision task - whether a text T entails a hypothesis H. Thus, in case of a negative answer, it is not possible to express that H is “almost entailed” by T. Partial textual entailment provides one possible approach to this issue. This paper presents an attempt to use word2vec model for recognizing partial (faceted) textual entailment...
Face recognition under surveillance circumstances still poses a significant problem due to low data quality. Nevertheless, automatic analysis is highly desired for criminal investigations due to the growing amount of security cameras worldwide. We suggest a face recognition system addressing the typical issues such as motion blur, noise or compression artifacts to improve low-quality recognition rates...
In recent years, the IoT application and the biometric-based authorization become popular. This paper proposes a face recognition system with high accuracy rate based on extended Local Binary Pattern, and applies it as an access control system on an IoT device which is always low-cost, low-power and small-footprint. The proposed face recognition system includes three parts, face detection, feature...
Deep face model learned on big dataset surpasses human for face recognition task on difficult unconstrained face dataset. But in practice, we are often lack of resources to learn such a complex model, or we only have very limited training samples (sometimes only one for each class) for a specific face recognition task. In this paper, we address these problems through transferring an already learned...
A novel and complete framework for face recognition with pose variations using only one image is proposed in this paper. Firstly, feature points on face images are located with view-based AAM (Active Appearance Model), based on which, alignment and normalization are operated on face images. Secondly, mapping from non-frontal images to frontal images is constructed based on the algorithm of linear...
As a fundamental and effective method, sparse representation based classification (SRC) has been applied to computer vision field for many years. However, SRC assumes that the training samples in each class contribute equally to the dictionary which will cause high residual errors and instability. In order to solve the problem and improve classification performance further, class specific centralized...
In this paper, a facial expression recognition algorithm based on Gabor and conditional random fields is proposed. Firstly, owing to the fact that in the existing databases, the number of people and images are relatively small, we established our own facial expression database, and some preprocessing methods are performed thereon. Secondly, Gabor features are extracted in five scales and eight directions...
In this paper we evaluate the performance of CNN in regards to face recognition for real world applications. In recent years, many high performance deep neural networks have been proposed to the face recognition world. These deep networks were trained by images provided by the internet, and they commonly are of good quality when facial expression and posture are not particularly complex. However,...
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