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A novel local texture feature extraction algorithm is proposed based on histogram of oriented gradient domain texture tendency (HOGTT). Classical HOG descriptor proves to be sensitive to rotation transformation, though it is insensitive to illumination and scale changes. The intrinsic texture tendency of tyre tread image has obvious consistency and is robust to different types of images transformation...
Human re-identification is an important component in many application domains especially the automatic surveillance system. This paper proposes a robust method to re-identify persons using their face shapes based on the Active Shape Model (ASM) and the Procrustes Shape Analysis (PSA). The ASM-based technique is used to extract landmark points of each face image, as the feature. Then, the Procrustes...
Deep learning has achieved great success in face recognition, however deep-learned features still have limited invariance to strong intra-personal variations such as large pose changes. It is observed that some facial attributes (e.g. eyebrow thickness, gender) are robust to such variations. We present the first work to systematically explore how the fusion of face recognition features (FRF) and facial...
Consider a face image data set from clients of a company and the problem of building a face recognition system from it. Video cameras can be used to acquire several images per client in order to maximize the robustness of the system. However, as the data set grows huge, the accuracy of the system might be seriously compromised since the number of negative samples for each user is increasing. We propose...
Fast and robust 3D reconstruction of facial geometric structure from a single image is a challenging task with numerous applications, but there exist two problems when applied "in the wild": the 3D estimates are unstable for different photos of the same subject; the 3D estimates are over-regularized and generic. In response, a robust method for regressing discriminative 3D morphable face...
Convolutional Neural Network (CNN) has led to significant progress in face recognition. Currently most CNNbased face recognition methods follow a two-step pipeline, i.e. a detected face is first aligned to a canonical one predefined by a mean face shape, and then it is fed into a CNN to extract features for recognition. The alignment step transforms all faces to the same shape, which can cause loss...
Biometrie systems have become a vital part of our present day automated systems. Every individual has its unique biometrie features in terms of Face, Iris and periocular regions. Identification/Recognition of a person by using these biometrie features is significantly studied over the last decade to build robust systems. The periocular region has become the powerful alternative for unconstrained biometrics...
Recently, sparse representation based classifiers (SRC) and collaborative representation based classifiers (CRC) have been shown to give very good performance under controlled scenarios. However, in practical applications, face recognition often encounters variations in illumination, expression, noise and occlusion, which cause severe performance degradation (due to the outliers in testing). In this...
With an increase in acceptance of face recognition systems, the desire for accurate biometric authentication — face recognition, has increased. Nowadays, the fundamental limitations of existing systems are, the vulnerabilities of false verification via a picture or simple video of a person. In this paper, it is inspired by how humans can perform reliable spoofing detection only based on the available...
Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors. In this paper, we propose a novel 3D shape representation learning approach, Directionally Convolutional Network (DCN), to solve the shape segmentation problem. DCN extends convolution operations from images to the surface mesh of 3D shapes. With DCN, we learn effective shape representations...
Despite remarkable progress of face analysis techniques, detecting landmarks on large-pose faces is still difficult due to self-occlusion, subtle landmark difference and incomplete information. To address these challenging issues, we introduce a novel recurrent 3D-2D dual learning model that alternatively performs 2D-based 3D face model refinement and 3D-to-2D projection based 2D landmark refinement...
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchorbased detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects:...
Robust Principal Component Analysis (RPCA) aims at recovering a low-rank subspace from grossly corrupted high-dimensional (often visual) data and is a cornerstone in many machine learning and computer vision applications. Even though RPCA has been shown to be very successful in solving many rank minimisation problems, there are still cases where degenerate or suboptimal solutions are obtained. This...
This paper describes a face recognition-based people tracking and re-identification system for RGB-D camera networks. The system tracks people and learns their faces online to keep track of their identities even if they move out from the camera's field of view once. For robust people re-identification, the system exploits the combination of a deep neural network- based face representation and a Bayesian...
Cross-resolution face recognition tackles the problem of matching face images with different resolutions. Although state-of-the-art convolutional neural network (CNN) based methods have reported promising performances on standard face recognition problems, such models cannot sufficiently describe images with resolution different from those seen during training, and thus cannot solve the above task...
This work presents an original real time, robust micro-expression detection algorithm. The algorithm analyses the movement modifications that occur around the most prominent facial regions using two absolute frame differences. Next, a machine learning algorithm is used to predict if a micro-expression occurred at a given frame t. Two classifiers were evaluated: decision tree and random forest classifier...
Previous models based on Deep Convolutional Neural Networks (DCNN) for face verification focused on learning face representations. The face features extracted from the models are applied to additional metric learning to improve a verification accuracy. The models extract high-dimensional face features to solve a multi-class classification. This results in a dependency of a model on specific training...
The goal of this article is to analyze the assurance of permissible quality indices in an interval system through the construction of the edge route and the use of D-partition method. There were obtained conditions for construction of D-partition domains on one and two edges of one face. On the basis of these conditions the technique for assurance of the permissible degree of robust stability and...
In video surveillance, face recognition (FR) systems seek to detect individuals of interest appearing over a distributed network of cameras. Still-to-video FR systems match faces captured in videos under challenging conditions against facial models, often designed using one reference still per individual. Although CNNs can achieve among the highest levels of accuracy in many real-world FR applications,...
Video-based face recognition (FR) is a challenging task in real-world applications. In still-to-video FR, probe facial regions of interest (ROIs) are typically captured with lower-quality video cameras under unconstrained conditions, where facial appearances vary according to pose, illumination, scale, expression, etc. These video ROIs are typically compared against facial models designed with high-quality...
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