Facial expression recognition in the wild, by fusion of deep learnt and hand-crafted features

Author:

Viswanatha Reddy G.,Dharma Savarni C.V.R.,Mukherjee Snehasis

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Experimental and Cognitive Psychology,Software

Reference42 articles.

1. Abdulrahman, M. & Eleyan, A. (2015). Facial expression recognition using Support Vector Machines. In 23nd Signal Processing and Communications Applications Conference (SIU).

2. Wild facial expression recognition based on incremental active learning;Ahmed;Cognitive Systems Research,2018

3. Basha, S. H., Dubey, S. R., Pulabaigari, V., & Mukherjee, S. (2019). Impact of fully connected layers on performance of convolutional neural networks for image classification, arXiv preprint arXiv:1902.02771.

4. Benitez-Quiroz, C. F., Srinivasan, R., & Martinez, A. M. (2016). Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In: Proceedings of IEEE international conference on Computer Vision and Pattern Recognition (CVPR16), Las Vegas, NV, USA.

5. Reliability and validity of machine vision for the assessment of facial expressions;Beringer;Cognitive Systems Research,2019

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