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Most dimensionality reduction methods are usually based on dissimilarity measurement of pixel intensities which can not obtain a more robust dissimilarity measurement. To address this problem, in this paper, we propose a novel robust dimensionality reduction method from Laplacian orientations. This method does not directly manipulate pixel intensity, which introduces Laplacian orientations, combined...
This paper proposes a face recognition method by combining mirror-like odd and even symmetrical images with their component features. First, use the mirror image transform and odd, even decomposition principle to extract odd and even symmetrical images. Second, extract eigenvectors with PCA (or kernel PCA) method of odd and even mirror symmetrical images, respectively. Third, combine the odd and even...
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...
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...
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...
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...
Support vector machine(SVM) means that structural risk minimization principle is used to substitute Empirical risk minimization principle. SVM has shown the excellent performance in pattern recognition. The kernel function is the core of SVM, with which SVM can help to resolve many kinds of non-linear classification problems. Different kernel models and parameters have different result in the performance...
Subspace analysis is an effective dimensional reduction approach for face recognition. Finding a suitable low dimensional subspace is a key step of subspace analysis, for it has a direct effect on recognition performance. In this paper, we propose a new subspace analysis method called center kernel unsupervised discriminant projection (CKUDP). The kernel trick is adopted to allow the efficient computation...
Multikernel learning (MKL) has recently received great attention in the field of computer vision and pattern recognition. The idea behind MKL is to optimally combine and utilize multiple kernels and features instead of using a single kernel in learning classifiers. This paper presents a novel framework for MKL problem by expanding the HessianMKL algorithm into multiclass-SVM with one-against-one rule...
Linear Discriminant Analysis (LDA) has been widely used in appearance-based face recognition. However, it requires lots of training samples for each person with respect to the large dimensionality of the image space, which is difficult to collect in reality. To overcome the severe constraint of training sample deficiency, approaches based on single training sample per person (SSPP) arise in the past...
This paper analyses the security of a recently proposed chaos based cryptosystem. It shows that the cryptosystem under study has weak diffusion and presents a cryptanalysis that allows the attacker to decrypt any encrypted image. More precisely, it proposes a divide-and-conquer attack that allows an attacker to recover the internal states of the cryptosystem and to use them in order to encrypt or...
Face recognition has become one of the hot research topics in pattern recognition and image processing in the recent several years, as a result of the wide application in the areas of security control and human-machine interaction. And it has been recognized as the most simplest and non-intrusive technology without hazardous problems, compared to other biometric recognition technology, such as fingerprint...
Canonical correlation analysis (CCA) has been widely used in pattern recognition and machine learning. However, both CCA and its extensions sometimes cannot give satisfactory results. In this paper, we propose a new CCA-type method termed sparse representation based discriminative CCA (SPDCCA) by incorporating sparse representation and discriminative information simultaneously into traditional CCA...
Even though facial expressions have universal meaning in communications, their appearances show a large amount of variation due to many factors, such as different image acquisition setups, different ages, genders, and cultural backgrounds etc. Collecting enough amounts of annotated samples for each target domain is impractical, this paper investigates the problem of facial expression recognition in...
A novel face recognition approach, modular kernel principal component analysis (MKPCA), combining the idea of modularity in a kernel method is proposed in this paper. In this technique, face images are divided into sub images (modular approach) and features are extracted from a high dimensional space formed using a Gaussian kernel. This method combines advantages of both modular PCA - more local features...
Most pregnant women are positive feeling and happy when the ultrasound scanning results the normal detection. However, if the finding gives unexpected anomaly detection, it is the cause of unnecessary anxiety and worry. Fetal neurobehavioral assessment is one of the antenatal assessments. The goal is to identify fetal being that is well or at risk and expected that the risk can be prevented or reduced...
Since Gabor features are robust to changes in illumination and facial expression and have been successfully applied for face recognition. The locality preserving projection (LPP) is nonorthogonal and makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) produces orthogonal basis functions and can have more locality preserving power than LPP. OLPP has more...
In this paper we provide a comparative study of several conventional face recognition methods (PCA, KPCA, GDA, SVM and RBF) that are suitable to work properly in multimodal systems. Performance of these systems is often influenced by various negative effects of the real-world environment. We evaluate the influence of varying illuminations and pose of faces on face recognition accuracy. Based on the...
Facial expression is one of the most important non-verbal behavioural cues in social signals. Constructing an effective face representation from images is an essential step for successful facial behaviour analysis. Most existing face descriptors operate on the same scale, and do not leverage coarse v.s. fine methods such as image pyramids. In this work, we propose the sparse appearance descriptors...
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