Efficient facial expression recognition via convolution neural network and infrared imaging technology
Abstract
Facial expression recognition is an important research topic in the field of human-machine interaction. Infrared imaging technology has illustrated good potential in the applications of facial expression recognition and computer vision. But the low-resolution spectral data has limited its applications, such as band overlap and random noises. To address the problems, a rapid blind restoration model with discrete beamlet transforms regularization is presented to reconstruct the infrared spectrum. To compare the sparsity between the observed infrared spectrum and ground-truth one in frequency domain, the discrete beamlet transforms is applied to analyze the different of their coefficients distributions. We propose an IR spectral deconvolution model with the sparsity coefficients regularization by L0-norm. We execute the proposed algorithm on the simulated and actual IR spectrum data, and the results demonstrate that can effectively suppress the Poisson noises and retain infrared spectral structure. The high-resolution IR spectrum can raise the recognition rate of facial expression classification task.
- Publication:
-
Infrared Physics and Technology
- Pub Date:
- November 2019
- DOI:
- 10.1016/j.infrared.2019.103031
- Bibcode:
- 2019InPhT.10203031W
- Keywords:
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- Infrared spectroscopy;
- Facial expression recognition;
- Regularization;
- Convolution neural network