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Local Binary Patterns (LBP) is a non-parametric descriptor whose aim is to efficiently summarize the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision, and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as(More)
Research in affective computing requires ground truth data for training and benchmarking computational models for machine-based emotion understanding. In this paper, we propose a large video database, namely LIRIS-ACCEDE, for affective content analysis and related applications, including video indexing, summarization or browsing. In contrast to existing(More)
Multidrug resistance (MDR) is one of the main obstacles in tumor chemotherapy. A promising approach to solving this problem is to utilize a nontoxic and potent modulator able to reverse MDR, which in combination with anticancer drugs increases the anticancer effect. Experiments were carried out to examine the potential of tetrandrine (Tet) as a(More)
In the context of content-based multimedia indexing gender identification based on speech signal is an important task. In this paper a set of acoustic and pitch features along with different classifiers are compared for the problem of gender identification. We show that the fusion of features and classifiers performs better than any individual classifier.(More)
This paper provides a description of the MediaEval 2015 “Affective Impact of Movies Task”, which is running for the fifth year, previously under the name “Violent Scenes Detection”. In this year’s task, participants are expected to create systems that automatically detect video content that depicts violence, or predict the affective impact that video(More)
The Local Binary Pattern (LBP) operator is a computationally efficient yet powerful feature for analyzing local texture structures. While the LBP operator has been successfully applied to tasks as diverse as texture classification, texture segmentation, face recognition and facial expression recognition, etc., it has been rarely used in the domain of Visual(More)
This paper presents an effective method for 3-D face recognition using a novel geometric facial representation along with a local feature hybrid matching scheme. The proposed facial surface description is based on a set of facial depth maps extracted by multiscale extended Local Binary Patterns (eLBP) and enables an efficient and accurate description of(More)
Visual content description is a key issue for machine-based image analysis and understanding. A good visual descriptor should be both discriminative and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. In this paper, we propose a new operator called the orthogonal combination of(More)
This paper presents a mesh-based approach for 3D face recognition using a novel local shape descriptor and a SIFT-like matching process. Both maximum and minimum curvatures estimated in the 3D Gaussian scale space are employed to detect salient points. To comprehensively characterize 3D facial surfaces and their variations, we calculate weighted statistical(More)