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To relieve the curse of dimensionality in functional magnetic resonance imaging (fMRI), we combine analysis of variance (ANOVA) with a support vector machine (SVM) to form a feature-based classification method. ANOVA is applied to find a more compact representation of the data by extracting features from fMRI images. A linear kernel SVM classifier is then trained on the selected features. Combining...
Face recognition for biometric purposes has an advantage of being a non-contact process. Various face recognition algorithms has been proposed in the literature. The face recognition system mainly consists of two steps i.e. feature extraction / reduction and classification. One of the most popular tool, Principal Component Analysis (PCA) is used for feature extraction. For classification purpose,...
The classical Camshift face tracking algorithm requires a higher environmental demand and is susceptible to the skin color interference. To solve the above problems, an improved Camshift tracking algorithm — ERC (Environmental Robust Camshift) is presented. Firstly, the RGB histogram equalization is applied in ERC to extend the hue differentiation between pixels; secondly, the statistical and spatial...
Facial pose grouping plays an important role in the video face recognition. In this paper, we present an unsupervised facial pose grouping approach via Garbor subspace affinity and self-tuning spectral clustering. First, we utilize the local normalization method to reduce the impact of uneven illuminations, and then extract the discriminative appearance features via Gabor wavelet representation. Next,...
We propose an autism spectrum disorder (ASD) prediction system based on machine learning techniques. Our work features the novel development and application of machine learning methods over traditional ASD evaluation protocols. Specifically, we are interested in discovering the latent patterns that possibly indicate the symptom of ASD underneath the observations of eye movement. A group of subjects...
Recognition under uncontrolled lighting conditions remains one of the major challenge for practical face recognition systems. In this work, we present an efficient and effective framework to improve the recognition performance from two aspects: image preprocessing and subspace representation. The step of image preprocessing is mainly used to eliminate the effects of illumination. The step of subspace...
A new approach for pain event detection in video is presented in this paper. Different from some previous works which focused on frame-based detection, we target in detecting pain events at video level. In this work, we explore the spatial information of video frames and dynamic textures of video sequences, and propose two different types of features. HOG of fiducial points (P-HOG) is employed to...
In this paper, we propose a new face image restoration method based on the features extracted from the noisy images given by the principal components of the noise covariance matrix. This technique deliberate the additive normal scattered degradation classic and use of a code shrinkage technique to remove noise from the images. The proposed work have been a large number of artificial neural networks...
In this paper, we present a head pose estimation method for unconstrained images using feature-based manifold embedding. The main challenge of manifold embedding methods is to learn a similarity kernel that is reflective of variations only due to head pose and ignore other sources of variation. To address this challenge, we have used the feature correspondences of identity-invariant Geometric Blur...
With the popularization of Internet access, institutions and parents have encountered serious problems to prevent access by employers and children to inappropriate content such as pornographic pages. The detection mechanisms try to circumvent or at least mitigate this problem by using of filters or mechanisms that enable the nudity detection in digital images. This paper proposes ANDImage, an adaptative...
CPU-GPU heterogeneous systems have become a mainstream platform in both server and embedded domains with ever increasing demand for powerful accelerator. In this paper, we present parallelization techniques that exploit both data and task parallelism of LBP based face detection algorithm on an embedded heterogeneous platform. By running tasks in a pipelined parallel way on multicore CPUs and by offloading...
We address the problem of video face retrieval in TV-Series, which searches video clips based on the presence of particular character, given one video clip of his/hers. This is tremendously challenging because on one hand, faces in TV-Series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs highly efficient representation...
In this paper, we propose an optimization scheme aiming at optimal nonlinear data projection, in terms of Fisher ratio maximization. To this end, we formulate an iterative optimization scheme consisting of two processing steps: optimal data projection calculation and optimal class representation determination. Compared to the standard approach employing the class mean vectors for class representation,...
In this paper we deal with the problem of frontal face detection using Support Vector Data Description (SVDD) to characterize textural attributes of faces. The SVDD classifier relies on PCA features of face samples to obtain a decision boundary around the face data without using information of negative examples (outliers). We analyze the performance of the classifier for different dimensionalities...
A person's face provides a lot of information such as age, gender and identity. Faces allow humans to estimate/ classify the age of other persons just by looking at their face. Researchers who carried out work in studying the process of age classification by humans conclude that humans are not so accurate in age classification; hence the possibility of developing facial age classification methods...
In this paper a framework is presented to deals with various aspects of face recognition like illumination, rotation and scaling. The proposed framework consists of three parts. In the first part Gabor filter is used over the thermal faces at different scales, locations, and orientations. In second part, the fixed point algorithm Fast ICA have been used over the Gabor filtered images to represent...
For which low frequency discrete cosine transform (DCT) coefficients retransforming based on contrast limiting adaptive histogram equalization (CLAHE) is proposed. Firstly, original images are divided into several non-overlapping blocks and CLAHE is used to do local contrast stretching so as to reduce noise. Then, illustration variation of face image is removed by reducing suit numbers of low frequency...
In clutter background, the performance of tracking target can be influenced by the factors such as illumination, camera angle. Meanwhile the target can be occluded by some obstacles in the background or be occluded by the target itself. To solve those problems, a multi-window tracking method is proposed, which represents the tracking target with several windows that each one corresponds with a tracker...
This research aims at studying the recognition accuracy and execution time that are affected by different dimensionality reduction methods applied to the biometric image data. We comparatively study the fingerprint, face images, and handwritten signature data that are pre-processed with the two statistical based dimensionality reduction methods: principal component analysis (PCA) and linear discriminant...
This paper presents a novel method to recognize subtle emotions based on optical strain magnitude feature extraction from the temporal point of view. The common way that subtle emotions are exhibited by a person is in the form of visually observed micro-expressions, which usually occur only over a brief period of time. Optical strain allows small deformations on the face to be computed between successive...
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