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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...
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...
An application of artificial vision and artificial neural networks techniques in face recognition, is presented. In order to do that, a set of images (frontal face photos) with different lighting conditions, gestures, accessories and distances is used. A stepwise algorithm allows to achieve a satisfactory results, obtaining the correct identification of images inside and outside the data set.
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...
Recent studies show that eyebrows can be used as a biometric or soft biometric for recognition. In some scenarios such as partially occluded or covered faces, they can be used for recognition. In this paper, we study eyebrow recognition using texture-based features. We apply features which have not been used before for eyebrow recognition such as 3-patch local binary pattern and WLD (Weber local descriptor)...
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...
Identification and apprehension of criminals by matching facial sketches with photographic faces is one of the major law enforcement applications of the modern world. Majority of the crime occur where there will not be any information available regarding the suspect. In such situation, forensic sketch artist who usually deal with the eyewitness of the crime or victim in order to draw the sketch that...
In this paper, we propose a pedestrian attribute recognition approach and a CNN-based person re-identification framework enhanced by pedestrian attributes. The knowledge of person attributes can help video surveillance tasks like person re-identification as well as person search, semantic video indexing and retrieval to overcome viewpoint changes with their robustness to the inherent visual appearance...
The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied in the wild, their 3D estimates are either unstable and change for different photos...
Facial change with age is always a problem in facial features matching for photos of ID cards. In order to solve this problem, a novel facial features matching method based on SIFT algorithm is proposed. Inspired by attention mechanism of the visual system, the facial features focuses on the four sub regions (left eye, right eye, nose and mouth), ignoring other unimportant regions, and different weights...
In this paper, we propose a topology preserving graph matching (TPGM) method for partial face recognition. Most existing face recognition methods extract features from holistic face images, yet faces in real-world unconstrained environments are usually occluded by objects or other faces, which cannot provide the whole face images for recognition. Latest keypoint-based partial face recognition methods...
Convolutional neural networks have significantly boosted the performance of face recognition in recent years due to its high capacity in learning discriminative features. In order to enhance the discriminative power of the deeply learned features, we propose a new supervision signal named marginal loss for deep face recognition. Specifically, the marginal loss simultaneously minimises the intra-class...
Growing number of surveillance and biometric applications seek to recognize the face of individuals appearing in the viewpoint of video cameras. Systems for video-based FR can be subjected to challenging operational environments, where the appearance of faces captured with video cameras varies significantly due to changes in pose, illumination, scale, blur, expression, occlusion, etc. In particular,...
Recently low-rank matrix decomposition (LR) and sparse representation classification (SRC) have been successfully applied to address the problem of face recognition. Low-rank matrix decomposition is employed as the first step of robust principal component analysis (RPCA), it is robust to illumination-contaminated image data. In this paper, we propose a novel method based on low-rank decomposition...
Face recognition in real scenarios is mainly affected by illumination variation and occlusion, and therefore in order to develop a robust face recognition system these issues should be handled simultaneously. To this aim, the steps involved in the presented framework are (i) computationally simple and efficient preprocessing chain that eliminates major effects of illumination variation and noise while...
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