The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
Recent advances in multiple-kernel learning (MKL) show the effectiveness to fuse multiple base features in object detection and recognition. However, MKL tends to select only the most discriminative base features but ignore other less discriminative base features which may provide complementary information. Moreover, MKL usually employ Gaussian RBF kernels to transform each base feature to its high...
Asymmetric facial expressions, such as a smirk, are strong emotional signals indicating valence as well as discrete emotion states such as contempt, doubt and defiance. Yet, the automated detection of asymmetric facial action units has been largely ignored to date. We present the first automated system for detecting spontaneous asymmetric lip movements as people watched online video commercials. Many...
This paper presents a method to recognize attentional behaviors from a head-mounted binocular eye tracker in triadic interactions. By taking advantage of the first-person view, we simultaneously estimate the first-person and third-person gaze. The first-person gaze is computed using an appearance-based method relying on local features. In parallel, head pose tracking allows determining the coarse...
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...
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...
A novel template matching method is proposed for eye detection. the classic template matching methods directly use images as templates. They are susceptible to variations in scale and light conditions. Moreover, the linear correlation coefficient is used to measure the matching degree without considering the higher-order statistics of images. Unlike the classic template matching, the projection coefficients...
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...
The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup...
In this paper, we propose a novel approach using Kernel Class-dependent Feature Analysis (KCFA) combined with facial color based features to tackle the problem of ethnicity classification on large scale face databases. In our approach, a new design of multiple filtered responses of the Kernel Class-dependent Feature Analysis is used for ethnicity classes. In order to improve the robustness of our...
We investigate the application of similarity-based classification to biometric recognition, interpreting similarity functions used in biometric systems (i.e., matching algorithms) as kernel functions. This leads us to formulate biometric recognition as a distinct two-class classification problem for each client, which can be solved even when no representation of biometric samples in a feature space...
The goal of face detection is to determine the presence of faces in arbitrary images, along with their locations and dimensions. As it happens with any graphics workloads, these algorithms benefit from data-level parallelism. Existing parallelization efforts strictly focus on mapping different divide and conquer strategies into multicore CPUs and GPUs. However, even the most advanced single-chip many-core...
Several studies explored the application of Discriminant analysis on Grassmann manifolds to tackle the image sets matching. But these methods suffer from not considering the local structure of the data. In this paper, a new method of face recognition which based on a graph embedding framework and geometric distance perturbation has been proposed. By introducing similarity graphs and maximal linear...
In order to reduce the original algorithm's dependence on the primary matrix and raise the classification accuracy, we combine the five order Newton method and the steepest descent method. We also introduce punishing factors into algorithm, and apply them to the core of the iterative process of the Fast ICA algorithm, an improved kernel independent component analysis algorithm (KICA) is proposed in...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.