Analysis and Modelling of Faces and Gestures: Second by Takeo Kanade (auth.), Wenyi Zhao, Shaogang Gong, Xiaoou Tang

By Takeo Kanade (auth.), Wenyi Zhao, Shaogang Gong, Xiaoou Tang (eds.)

During the final 30 years, face attractiveness and similar difficulties resembling face detection/tracking and facial features attractiveness have attracted researchers from either the engineering and psychology groups. moreover, vast examine has been performed to review hand and physique gestures. the knowledge of ways people understand those vital cues has major medical worth and broad functions. for instance, human-computer interplay, visible surveillance, and clever video indexing are energetic software components. Aiming in the direction of placing such remarkable belief strength onto desktops, researchers have made monstrous development. even though, technological demanding situations nonetheless exist in lots of features. Following a layout just like the IEEE foreign Workshop on research and Modeling of Faces and Gestures (AMFG) 2003, this one-day workshop (AMFG 2005) supplied a centred overseas discussion board to assemble famous researchers and examine teams to check the prestige of popularity, research and modeling of faces and gestures, to debate the demanding situations that we face, and to discover destiny instructions. total, 30 papers have been chosen from ninety submitted manuscripts. the subjects of those papers diversity from function illustration, powerful popularity, studying, and 3D modeling to psychology. furthermore, invited talks got, by way of Prof. Kanade and Dr. Phillips. The technical application used to be geared up into 4 oral periods and poster periods. This workshop should not have been attainable with no the well timed experiences supplied via the individuals of the Technical software Committee less than a good time table. October 2005 Wenyi Zhao Shaogang Gong Xiaoou Tang

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Extra info for Analysis and Modelling of Faces and Gestures: Second International Workshop, AMFG 2005, Beijing, China, October 16, 2005. Proceedings

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The 3D recognition result and the 2D recognition result are each produced without reference to the other modality. It is desirable to explore synergies of the two modalities at the feature level as well [14]. The work presented here explores such synergies in the proposed framework of AdaBoost learning (with LBP feature). This is new for solving the problem of 3D+2D face fusion. In this paper, we propose a systematic framework for fusing 2D and 3D information at both feature and decision levels.

5 Conclusions In this paper we introduce a novel kernel correlation filter method applied in the redundant class-dependence feature analysis (CFA) approach to perform robust face recognition in the Face Recognition Grand Challenge (FRGC) data set. Under the CFA framework, the class-dependence features can be extracted for each target image and the query image and the nearest neighbor rule is applied for classification. The linear correlation filter based CFA approach is extended to the nonlinear correlation filter based CFA approach with the kernel methods.

4 Kernel Methods of Correlation Filters Polynomial correlation filter (PCF) [19] had been developed to generate nonlinear correlation filter classifiers. g. ) are applied to each pixel of the image and the filters are developed based on the transformed images to optimize a performance criterion of interest. It is shown [19] that the polynomial correlation filter outperforms the linear correlation filter. In this paper, we introduce a new method to extend the linear ECP-SDF and OTF correlation filters to non-linear correlation filters using kernel methods.

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