Enhancing the Performance of Active Shape Models in Face Recognition Applications

Behaine, Carlos Alberto Ramirez; Scharcanski, Jacob

doi:10.1109/tim.2012.2188174

Cited by 38 publications

(51 citation statements)

References 9 publications

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“…(5) According to the comparison and analysis of the traditional method of welding seam location and the algorithm of this paper [11], the accuracy of the weld location is calculated and compared according to different algorithms, as shown in Table 1 and Fig. 5 According to the running time of different algorithms, the results are shown in Table 2, and Fig.6.…”

Section: Experiments and Analysismentioning

confidence: 99%

Research on Weld Location Algorithm based on Active Shape Model

Liu¹,

Jiang²

2017

Proceedings of the 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)

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Abstract. In order to improve the welding quality and avoid the welding deviation, a weld seam location algorithm based on active shape model is proposed. The local feature model is set up to detect the image, and then each feature point is used to construct the local feature. Finally, the new location of the model is searched. In the experiment, the training samples are selected to model, and the test samples are used to search and locate. By comparative experiments show that the proposed algorithm has the characteristics of high accuracy, small error, and can meet the requirements of the accuracy of the laser weld positioning system.

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Section: Experiments and Analysismentioning

confidence: 99%

Research on Weld Location Algorithm based on Active Shape Model

Liu¹,

Jiang²

2017

Proceedings of the 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)

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“…Several technological developments rely on the acquisition and processing of images and videos to extract and enhance information (Scharcanski et al, 1993;Cavalcanti et al, 2010), in areas ranging from engineering Dodson, 1966, 2000;Scharcanski, 2005) and medicine (Wong et al, 2011) to biometric authentication (Behaine and Scharcanski, 2012;Verdoolaege et al, 2014). In particular, the growing processing capability and the improvement of the camera quality of consumer electronic devices (Google, 2016), such as smartphones and laptops, allow to obtain a wide range of vision-based measurements in a variety of areas, such as monitoring food intake, assisting the visual impaired, biometric user authentication, and in several other medicine and engineering applications.…”

Section: Reliable Measurements Via Computer Visionmentioning

confidence: 99%

Bringing Vision-Based Measurements into our Daily Life: A Grand Challenge for Computer Vision Systems

Scharcanski

2016

Front. ICT

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Bringing computer vision into our daily life has been challenging researchers in industry and in academia over the past decades. However, the continuous development of cameras and computing systems turned computer vision-based measurements into a viable option, allowing new solutions to known problems. In this context, computer vision is a generic tool that can be used to measure and monitor phenomena in wide range of fields. The idea of using vision-based measurements is appealing, since these measurements can be fast and easy to obtain and handle by humans. On the other hand, these vision-based measurements need to be at least as precise and accurate as those obtained by more traditional measuring systems. This article discusses the perspectives and challenges of introducing vision-based measurements into our daily life.

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“…ASM model position feature points in the image relying on global shape model and local appearance model of each feature point. For AAM model, it establishs global shape model and texture model, through the shape changes in the image sampling, make the sampling image texture and texture model to match [6,7,[11][12][13][14]. ASM model is affected deeply by the initial value, if the initial value is near the global optimal solution, ASM model can be fast convergent.…”

Section: A Research Statusmentioning

confidence: 99%

Feature Extraction of Single Face Image Based Active Appearance Model

Cao¹

2016

Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling

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Abstract-Face as the most direct and important carrier of human's daily emotional expression and information exchange, with its peculiar to different from other organs and parts of the universality and ease of use as mathematical modeling of the object of study, and many research algorithm has been applied to the photographing, face recognizing and face rebuilding, etc. Currently, there are face recognizing and face rebuilding two research direction around face, this paper studies the AAM algorithm based on front face feature points extraction, and using MATLAB to achieve programming. Results show that this method can quickly locate face to extract the complete face shape.

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Enhancing the Performance of Active Shape Models in Face Recognition Applications

Cited by 38 publications

References 9 publications

Research on Weld Location Algorithm based on Active Shape Model

Research on Weld Location Algorithm based on Active Shape Model

Bringing Vision-Based Measurements into our Daily Life: A Grand Challenge for Computer Vision Systems

Feature Extraction of Single Face Image Based Active Appearance Model

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