In this working note, we mainly focus on the image annotation subtask of imageCLEF 2015 challenge that BUAA-iCC research group participated. For this task, we firstly explore textual similarity information between eac...
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In this working note, we mainly focus on the image annotation subtask of imageCLEF 2015 challenge that BUAA-iCC research group participated. For this task, we firstly explore textual similarity information between each test sample and predefined concept. Subsequently, two different kinds of semantic information are extracted from visual images: visual tags using generic object recognition classifiers and visual tags relevant to human being related concepts. For the former information, the visual tags are predicted by using deep convolutional neural network (CNN) and a set of support vector machines trained on imageNet, and finally transferred to textual information. For the latter visual information, human related concepts are extracted via face and facial attribute detection, and finally transferred to similarity information by using manually designed mapping rules, in order to enhance the performance of annotating human related concepts. Meanwhile, a late fusion strategy is developed to incorporate aforementioned various kinds of similarity information. Results validate that the combination of the textual and visual similarity information and the adopted late fusion strategy could yield significantly better performance.
The local space-time feature is an effective way to represent video data and achieves state-of-the-art performance in action recognition. However, in majority of cases, it only captures the static or dynamic cues of t...
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This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression recognition (FER) problem. In total, 26 different feature extractors are included, of which six are se...
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ISBN:
(纸本)9781479981748
This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution is to provide a solid overview for future research.
In an automatic face recognition system, it still remains a challenge to improve the robustness to aging. In this paper, we present a novel approach to address age invariant face recognition, by formulating it as a gr...
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In an automatic face recognition system, it still remains a challenge to improve the robustness to aging. In this paper, we present a novel approach to address age invariant face recognition, by formulating it as a graph matching problem. In contrast to the majority of tasks in the literature that only make use of robust texture features, this method generates a graph from a set of fiducial landmarks of each face, which captures the texture clues that tend to be stable in a period as well as the common facial geometry configuration. The nodes of the graph denote the texture of a face area around a landmark, and the edges correspond to the geometry topology of the face. For each area, the age invariant texture information is extracted by a discriminative and compact feature encoded in the Local Gabor Binary Pattern Histogram Sequence (LGBPHS) projected in an LDA subspace. An objective function is then designed to match graphs for the purpose of registration and identification. Experiments are carried out on the FG-NET Aging database, and the results achieved outperform the state of the art ones, which clearly demonstrate the effectiveness and robustness of the proposed method in face recognition across age variations.
The local space-time feature is an effective way to represent video data and achieves state-of-the-art performance in action recognition. However, in majority of cases, it only captures the static or dynamic cues of t...
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ISBN:
(纸本)9781479957521
The local space-time feature is an effective way to represent video data and achieves state-of-the-art performance in action recognition. However, in majority of cases, it only captures the static or dynamic cues of the image sequence. In this paper, we propose a novel kinematic descriptor, namely Static and Dynamic fEature Velocity (SDEV), which models the changes of both static and dynamic information with time for action recognition. It is not only discriminative itself, but also complementary to the existing descriptors, thus leading to more comprehensive representation of actions by their combination. Evaluated on two public databases, i.e. UCF sports and Olympic Sports, the results clearly illustrate the competency of SDEV.
In this paper, we present an efficient human parsing method which estimates human body poses from 2D images. Firstly we propose an edge sketch representation, which enhance critical information for pose estimation and...
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In recent years, the hand dorsal vein has received increasing attentions in the domain of biometrics. This paper presents a novel approach for hand dorsal vein identification based on shape description of the venous n...
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Biometrics and information hiding, as two different yet promising techniques for individual identification and digital media protection, have been extensively studied in the latest decade. Recently, hybrid approaches ...
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Automatic Facial Expression recognition (FER) is one of the most active topics in the domain of computer vision and pattern recognition. In this paper, we focus on discrete facial expression recognition by using 4D da...
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