Abstract
The face though seems an easy object to be recognized by retina, but the artificial intelligence is not yet intelligent enough to do the task easily. As the source of a face is generally an image capturing object, there are a lot of variations and complexions that persists with the image like (e.g., noise, rotation). There are many techniques that use some algorithms to find similarity in face model and the test image, and most of them are successful on their part to attain better test similarities. However, considering the diverse scale of applications and mode of image sourcing, a single algorithm cannot get maximum efficiency everywhere. Even after using the best algorithm for a particular task, an application has to counter with challenges of face recognition. This paper analyzes the hybrid approach of Gabor wavelet and Harris corner features for facial expression recognition. A subspace is created by this algorithm for training of feature vectors, and random forest classifier calculates the similarity score for performance evaluation which will provide improved results in terms of recognition accuracy.
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Tiwari, K., Patel, M. (2020). Facial Expression Recognition Using Random Forest Classifier. In: Mathur, G., Sharma, H., Bundele, M., Dey, N., Paprzycki, M. (eds) International Conference on Artificial Intelligence: Advances and Applications 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1059-5_15
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DOI: https://doi.org/10.1007/978-981-15-1059-5_15
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