Abstract
The robust recognition of a person’s emotion from images is an important task in human-machine interaction. This task can be considered a classification problem, for which a plethora of methods exists. In this paper, the emotion recognition performance of two fundamentally different approaches is compared: Classification based on hand-crafted features against deep learning. This comparison is conducted by means of well-established datasets and highlights the benefits and drawbacks of each approach.
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CITATION STYLE
Dunau, P., Huber, M. F., & Beyerer, J. (2019). Comparison of angle and size features with deep learning for emotion recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11401 LNCS, pp. 602–610). Springer Verlag. https://doi.org/10.1007/978-3-030-13469-3_70
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