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Non-negative Matrix Factorization (NMF) has been widely studied and applied to variant computer vision tasks, such as image clustering and pattern classification. Meanwhile, real world stimuli for human neural system (e.g., face images) are usually represented as high-dimensional data vectors rely on graph embedding in original Euclidean space. Thus, the traditional NMF and its variants exhibit weakness...
In human-machine interaction, the captured faces are usually low-resolution (LR), which will degrade the performance of the following face detection and face recognition. Face hallucination is the technology of obtaining a high-resolution (HR) face from its observed LR one. In order to recover more facial details, we propose a novel method called kernel locality-constraint adaptive iterative neighbor...
Biometrics, due to their uniqueness and accuracy are being widely used for authentication and identification. In multimodal biometrics more than one trait of an individual is fused. Multimodal biometrics is of different types, multi algorithmic, multi instance and multi sensorial. This paper combines two biometric traits namely face and fingerprint. Face is being processed using two algorithms, PCA...
Occluded images often affected the recognition rates in face recognition, thus the occlusion should be checked out and given a little weighting coefficient so as to weaken its impact on the recognition rate as much as possible. The traditional algorithms often use the reconstruction error operator based on principal component analysis (PCA) to estimate the weight for occluded face, which often need...
Faces express many social indications, including gender, ethnicity, age, expression and identity, most of them have drawn thriving attention from various research communities, for instance neuroscience, computer science and psychology. In this paper, we propose a new approach to classify gender and ethnicity by merging both texture and shape features extracted from face images. Gabor filter is used...
In recent years, we can observe an increasing use of biometric technology in our daily lives. Face recognition has several advantages over other biometric modalities, since that it is natural, nonintrusive, and it is a task that humans perform routinely and effortlessly. Following a recent trend in this research field, this paper focuses on a part-based face recognition, exploring and evaluating specific...
The kernel trick becomes a burden for some machine learning tasks such as dictionary learning, where a huge amount of training samples are needed, making the kernel matrix gigantic and infeasible to store or process. In this work, we propose to alleviate this problem and achieve Gaussian RBF kernel expansion explicitly for dictionary learning using Fastfood transform, which is an approximation of...
Near infrared (NIR) partial face images acquired in iris recognition in less constrained environments contain plentiful identity information which have not been fully exploited. In this paper, a NIR partial face recognition (PFR) algorithm is designed according to the characteristics of NIR partial face images acquired in iris recognition systems. In the preprocessing stage, the eye corners are utilized...
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named Local Binary Pattern Network (LBPNet), is proposed to efficiently extract and compare high-level over-complete features in multilayer hierarchy. The LBPNet retains the same topology of Convolutional Neural Network (CNN) — one of the most...
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Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem. This paper proposes an approach that couples a deep CNN-based approach with a low-dimensional discriminative embedding step, learned using triplet probability constraints to address the unconstrained face verification problem. Aside from yielding performance improvements,...
Airborne cameras on low-flying unmanned vehicles introduce new privacy challenges due to their mobility and viewing angles. In this paper, we focus on face recognition from airborne cameras and explore the design space to determine when a face in an airborne image is inherently protected, that is when an individual is not recognizable. Moreover, when individuals are recognizable by facial recognition...
In this paper, a robust technique to construct feature vector for gender classification has been proposed. Discrete Wavelet transform is used in concatenation with Discrete Cosine transform to form the feature vector. Initially, multi-level Discrete Wavelet transform is applied to images to obtain the approximation coefficients of image. Discrete Cosine transform are then calculated for the obtained...
Face alignment is very crucial to the task of face attributes recognition. The performance of face attributes recognition would notably degrade if the fiducial points of the original face images are not precisely detected due to large lighting, pose and occlusion variations. In order to alleviate this problem, we propose a spatial transform based deep CNNs to improve the performance of face attributes...
In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag—positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative...
This paper presents an illumination invariant face recognition system that uses local directional pattern descriptor and modular histogram. The proposed Modular Histogram of Oriented Directional Features (MHODF) is an oriented local descriptor that is able to encode various patterns of face images under different lighting conditions. It employs the edge response values in different directions to encode...
Deep neural networks (DNNs) have been successfully applied in the fields of computer vision and pattern recognition. One drawback of DNNs is that most of existing DNNs models and their variants usually need to learn a very large set of parameters. Another drawback of DNNs is that DNNs does not fully take the class label and local structure into account during the training stage. To address these issues,...
In this paper, an asymmetric kernel is proposed for extracting sparse features from two-dimensional visual face images for identity recognition. Essentially, the kernel consists of an inner product of two vectors where one of them has been raised to power terms element-wise. The impact of such a power term is suppression of less influential features where only relevant ones are used for estimation...
Machine learning from brain images is a central tool for image-based diagnosis and diseases characterization. Predicting behavior from functional imaging, brain decoding, analyzes brain activity in terms of the behavior that it implies. While these multivariate techniques are becoming standard brain mapping tools, like mass-univariate analysis, they entail much larger computational costs. In an time...
A new technique to construct feature vector for gender classification is proposed in this paper. Here, new feature reduction technique is used to remove the irrelevant features of images. Feature reduction also helps in reducing the over fitting problem of the dataset. KPCA is a kernel based PCA which maps data from original space to non-linear feature space. Kernel trick helps in reducing the expensive...
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