Human Face Reaction Recognition with Attention Mechanism using CNN

Abstract

Face expression recognition is a challenging task because of various unconstrained reactions that the users may experience. Although existing framework have been practically flawless on investigating compelled frontal faces, they neglect to perform when some piece of appearances is blocked or covered up. So, considering that, we have proposed a convolution neutral network (CNN) along with attention mechanism (ACNN) that can focus on the most discriminative un-occluded regions on the face and it can address the issue that occurred in the existing system. ACNN automatically detects or perceives the hidden facial patches and pays attention only to the unblocked and patches that could be informative. The ACNN can concentrate on unmistakable just as unhindered districts in facial picture. Considering unique facial features, we propose an alternative version of ACNN called pACNN that crops patches from the last convolution feature maps. In our project we have made 16 patches each of size 4*4 from the convolution feature maps. For every detected patch, PG-Unit calculate weight of the given patch. The proposed ACNN is to be trained on both types of occlusions real and synthetic.