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Gender identification is a new domain in image recognition. Gender identification of human face is to judge one's gender according to his/her face features. The article adopted local binary pattern (LBP) algorithm to build feature subspaces, and processed data using Support Vector Machine (SVM) learning models. Experiments showed that integration of LBP algorithm with linear SVM and integration of...
Face Recognition is among the most widely studied problems in computer vision and Pattern Recognition. Face has many advantages like permanence, accessibility and universality. It is still now not solved in literature. Several approaches are proposed to overcome with problems including; changing posed, emotional states, and illumination variation, etc. Geometric approaches which used as example distance...
In this paper, we present a convolutional neural network (CNN) approach for the face verification task. We propose a “Siamese” architecture of two CNNs, with each CNN reduced to only four layers by fusing convolutional and subsampling layers. Network training is performed using the stochastic gradient descent algorithm with annealed global learning rate. Generalization ability of network is investigated...
Considering the limit that marginal fisher analysis(MFA) can't take advantage of the discriminant information in the training samples, this paper proposed a semi-supervised dimensionality reduction based on kernel marginal fisher analysis and sparsity preserving. The new algorithm firstly gets the sparse reconstruction of the samples. Secondly it uses the samples with labels to construct the intra-class...
This paper presents an approach for face recognition system based on independent component analysis (ICA) and support vector machine(SVM). The ICA is a feature extraction technique for isolating a multivariate signal into additive subcomponents by considering that the hidden components are non-Gaussian signals. It has been mainly used on the problem of blind signal separation, while support vector...
Traditional approaches to create sensor-level maps from magnetoencephalographic (MEG) data rely on mass-univariate methods. In order to overcome some limitations of univariate approaches, multivariate approaches have been widely investigated, mostly based on the paradigm of classification. Recently a multivariate two-sample test called kernel two-sample test (KTST) has been proposed as an alternative...
The information fusion of face and palmprint biometrics using local features is investigated at feature level. The proposed method uses local information extracted from local region of biometric image which has rich statistical information. The texture of each region is processed using multiresolution analysis with different orientations and scales. The feature dimensionality of each region is reduced...
Face recognition and feature detection plays a vital role in various applications such as human computer interaction, face tracking, video surveillance and face recognition. Efficient face recognition algorithms are required for applying to those tasks. Recently face recognition is attracting a big attention in the social application and also authentication. Face recognition makes hackers virtually...
In this paper we investigate the use of Multitask Learning (MTL) methods to model the commonalities and variations across a set of facial action units (AUs) and also learn the classifiers for detection of multiple AUs simultaneously by exploiting their inner-relations. We studied three variants of MTL algorithms, the Regularized MTL (RMTL), the Multitask Feature Learning (MTFL) and the Alternating...
Facial expressions are important in human computer interaction, because the machine can thereby understand human reactions and act accordingly. Facial expressions act like a nonverbal communication cues in human-human or human computer interactions. In noises environment, getting visual data is difficult. According to the relative bin sub-image based studies, high dimensionality is affecting the system...
This paper proposes a biometric authentication system based on feature level fusion of face and fingerprint modalities. The proposed method utilizes Gabor filter bank with two scales and eight orientations, to extract directional features from source data. Usage of a small set of Gabor filters typically reduces the system processing time. To introduce a good discriminating ability and to avoid curse...
The paper presents a joint framework for face hallucination incorporating face deblurring and registration. The joint framework not only directly hallucinates low resolution faces, but also deblurs and aligns low resolution faces iteratively to improve the performance of face hallucination. Without the need for accurate face registration and prior knowledge of blurring kernels, it is robust to errors...
Face recognition is a key biometric method aiming at identifying individuals by the features of face. Due to the challenges facing face recognition from 2D images, researchers have resorted to 3D face recognition. Our work in this paper is motivated by the recent and remarkable success of heat-based features for 3D object classification and retrieval. We propose an approach for 3D face recognition...
Random Subspace Method (RSM) has been demonstrated as an effective framework for gait recognition. Through combining a large number of weak classifiers, the generalization errors can be greatly reduced. Although RSM-based gait recognition system is robust to a large number of covariate factors, it is, in essence an unimodal biometric system and has the limitations when facing extremely large intra-class...
Three-dimensional (3D) facial recognition techniques using 3D range data have been widely applied in last years. Facial expression recognition approaches using 2D static images or video-sequences have also been extensively used. However, there are few works on facial expression recognition using 3D data. Although the computational cost of processing 3D information is higher than for 2D, the problems...
Accurate head pose estimation is significant for many applications such as face recognition and human-computer interaction. In this paper, we treat the head pose estimation as a classification problem and employ the Lie Algebrized Gaussians (LAG) feature as the representation approach for head image. The LAG feature, which is built on Gausssian Mixture Model (GMM), has the capability to preserve the...
The human face is one of the most popular characteristic which can be used in the biometric security system to identify or verify a user. Face is an acceptable biometric modality because it can be captured from a distance, even without physical contact of the user being identified. Thus the identification or verification does not require cooperation of the user. Recognition systems based on human...
This paper presents a parallel method for EBGM face recognition. Compared with other methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), EBGM has the advantage of higher accuracy, however, with more computational time and memory usage, which also mean less practicability. We propose a parallel method for EBGM by balancing the unit of images. We distribute the...
Age estimation via face images has recently attracted a lot of researches in computer vision, due to its many potential applications. In this paper, a novel Bisection Search Tree (BST) algorithm is proposed for face age estimation, based on the idea of Divide and Conquer. Different from those conventional classification or regression approaches, the BST first constructs a binary tree according to...
Key point-based methods are used in visual tracking applications. These methods often model the target as a collection of key point descriptors. Target localization on subsequent frames is thus a complex task that involves detecting key points, computing descriptors, matching features, and checking match consistency to update the reference model adequately and avoid tracker drifts. This work aims...
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