Abstract
In this research, a novel approach for emotion detection is exploited by taking the Accelerated Kaze (A-Kaze) features for emotion recognition. The Kaze Features work in a way such that object boundaries can be preserved by making blurring locally adaptive to the image data without severely affecting the noise-reducing capability of the Gaussian blurring, thereby increasing the accuracy of the system. After extracting the Kaze features, GMM is constructed and thus a Fisher Vector representation is made. The extracted features are passed through an SVM detector. An efficiency of 87.5% has been shown thus proving that Kaze can also be used effectively in the field of facial image processing.
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Vaish, A., Gupta, S. (2018). Employing Kaze Features for the Purpose of Emotion Recognition. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_65
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DOI: https://doi.org/10.1007/978-981-10-7871-2_65
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