Web Enabled Spontaneous Facial Expression Database (WESFED): Challenges and Design

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Published Jul 1, 2019
https://doi.org/10.47164/ijngc.v10i2.161
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Volume 10, Issue 2, July 2019

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Mahesh M Goyani
Assistant Professor, Department of Computer Engineering, Government Engineering College, Modasa Gujarat, India.

Abstract

Accurate Facial Expression Recognition (FER) can lead to improved man-machine interaction. The success of pattern recognition depends heavily on the amount and the quality of training samples. Over the time, many expression datasets have been published, but most of them are designed under controlled environment. Expressions are faked by subjects with peak intensity in front of the high-resolution camera. For the realistic use, the system should be able to cope with the spontaneous expressions, even in low-resolution environment. Although significant work has been done in the field of FER, a generalization of investigated methods is still unknown. Due to the multidimensional diversities in race, ethnicity and surrounding environment, it becomes difficult to design facial expression image dataset, which can serve as a benchmark tool for the research community. Existing dataset provides the baseline for the research, but they do not guarantee the real time use of systems trained with those datasets. In this paper, we present a 2D static Web Enabled Spontaneous Facial Expression Database (WESFED), which covers almost every type of diversities necessary to build real life expression recognition system. In this dataset, we overcome most of the limitations of existing dataset by incorporating variations in ethnicity, age, gender, illumination, inplane and out of plane rotation of the face. The work addresses the issues that needs to be considered while creating database. In addition, state-of-the-art datasets are also reviewed with their merits and demerits.

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How to Cite
Mahesh M Goyani. (2019). Web Enabled Spontaneous Facial Expression Database (WESFED): Challenges and Design. International Journal of Next-Generation Computing, 10(2), 139–151. https://doi.org/10.47164/ijngc.v10i2.161
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