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Pedestrian detection is one of the challenging research topics in computer vision and efficient feature representation of a pedestrian attracts more and more attention. Traditional features such as Histogram of Oriented Gradients (HOG) were widely used in pedestrian detection, but because of their poor texture description ability, these feature based methods cannot achieve satisfactory pedestrian...
In this paper, we present a large-scale database consisting of low cost Kinect 3D face videos, namely Lock3DFace, for 3D face analysis, particularly for 3D Face Recognition (FR). To the best of our knowledge, Lock3DFace is currently the largest low cost 3D face database for public academic use. The 3D samples are highly noisy and contain a diversity of variations in expression, pose, occlusion, time...
Face recognition at a distance is still a challenging problem due to the low resolution face images resulting from the remote distance. To motivate researches on the problem and make up for the shortage of existing databases, we introduce MDCI database in this paper. The database contains 677 videos and 9734 pictures from 155 subjects captured by five different cameras, at four kinds of distances...
Identifying subjects with variations caused by poses is one of the most challenging problems in face recognition, essentially, a misalignment problem. In this paper, we propose a simple, practical but effective continuous pose normalization method to handle pose variations. First, 2D-3D correspondence is constructed based on five facial landmarks of query image. A single reference 3D mesh is projected...
This paper presents a new method for 3D face pose tracking in arbitrary illumination change conditions using color image and depth data acquired by RGB-D cameras (e.g., Microsoft Kinect, Asus Xtion Pro Live, etc.). The method is based on an optimization process of an objective function combining photometric and geometric energy. The geometric energy is computed from depth data while the photometric...
In this paper, we present an approach for automatically convert images from 2D to 3D. The algorithm uses a color + depth dataset to estimate a depth map of a query color image by searching structurally similar images in the dataset and fusing them. Our experimental results indicate that the inclusion of a retinex based stage for the query image and the dataset images improves the performance of the...
Face recognition (FR) across illumination variations endeavors to alleviate the effect of illumination changes on human face, which remains a great challenge in reliable FR. Most prior studies focus on normalization of holistic lighting intensity while neglecting or simplifying the mechanism of image color formation. In contrast, we propose in this paper a novel approach for lighting robust FR through...
Human facial expressions have been extensively studied using 2D static images or 2D video sequences. The main limitations of 2D-based analysis are problems associated with large variations in pose and illumination. Therefore, an alternative is to utilize depth information, captured from 3D sensors, which is both pose and illumination invariant. The Kinect sensor is an inexpensive, portable, and fast...
The research on depth map is becoming a focus of image understanding and computer vision. In this paper, depth map is introduced to enhance the performance of face recognition and a novel multi-modal 2D + 3D method is proposed. First of all, we propose a new local feature descriptor called Enhanced Local Mixed Derivative Pattern (ELMDP). Then, this feature descriptor is applied on the 2D intensity...
An efficient face recognition system should recognize faces in different views and poses. The efficiency of a human face recognition system depends on the capability of face recognition in presence of changes in the appearance of face due to expression, pose and illumination. A novel algorithm which utilizes the combination of texture and depth information based on Modular PCA to overcome the problem...
Large pose and illumination variations are very challenging for face recognition. The 3D Morphable Model (3DMM) approach is one of the effective methods for pose and illumination invariant face recognition. However, it is very difficult for the 3DMM to recover the illumination of the 2D input image because the ratio of the albedo and illumination contributions in a pixel intensity is ambiguous. Unlike...
In this paper, we proposed a Medial Axis Distance (MAD) measure for body part detection with single depth image only. First of all, we extracted the skeleton line of human body based on the detected human body silhouette. Using the space information of pixels on the skeleton line and the silhouette, we determined a human body center point on the skeleton line. Then we proposed the MAD measure which...
Pose variation is one of the key challenges for practical face recognition problem. Face recognition under well-controlled settings, like frontal face and good illumination, achieved high performance. But it fails when they are directly adopted to face recognition with large pose change. In this paper, we propose a novel framework using the combination of SIFT and alignment error (SIFT-AE) to perform...
Recent researches in three-dimensional (3D) face recognition area have proved that, 3D face recognition methods, achieve better accuracy than its 2D counterpart. One of the main advantages of 3D face recognition methods is that it measures the geometry of rigid features on the human face, due to which it becomes an invariant to illumination, expressions and rotations of head (pose variation). But...
Face photographs, videos or masks can be used to spoof face recognition systems. Recent studies show that face recognition systems are vulnerable to these attacks. In this paper, a countermeasure technique, which analyzes the reflectance characteristics of masks and real faces, is proposed to detect mask attacks. There are limited studies on countermeasures against mask attacks. The reason for this...
In this paper, we propose a novel generative method which could generate images with different illuminations by using a single front-lighted sample. The generative method is based on the linear Lambertian property and requires a bootstrap set with multiple subjects and specific illuminations for each subject. During the generation process, we also propose a scale decomposition method to retain the...
Vulnerability to spoofing attacks is a serious drawback for many biometric systems. Among all biometric traits, face is the one that is exposed to the most serious threat, since it is exceptionally easy to access. The limited work on fraud detection capabilities for face mainly shapes around 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices. A significant...
In this paper we present a robust face authentication by the fusion of two local methods features and multi-sensors (3D and 2D information). First we fix the rotations of the head in all 3D by Iterative Closest Point (ICP), and then we present our algorithm preprocessing. For feature extraction we use the Extended Local Binary Patterns (E-LBP) and Statistic local features proposed (SLF) based on the...
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