  Several computer algorithms for recognition of visible human emotions are
compared at the web camera scenario using CNN/MMOD face detector. The
recognition refers to four face expressions: smile, surprise, anger, and
neutral. At the feature extraction stage, the following three concepts of face
description are confronted: (a) static 2D face geometry represented by its 68
characteristic landmarks (FP68); (b) dynamic 3D geometry defined by motion
parameters for eight distinguished face parts (denoted as AU8) of personalized
Candide-3 model; (c) static 2D visual description as 2D array of gray scale
pixels (known as facial raw image). At the classification stage, the
performance of two major models are analyzed: (a) support vector machine (SVM)
with kernel options; (b) convolutional neural network (CNN) with variety of
relevant tensor processing layers and blocks of them. The models are trained
for frontal views of human faces while they are tested for arbitrary head
poses. For geometric features, the success rate (accuracy) indicate nearly
triple increase of performance of CNN with respect to SVM classifiers. For raw
images, CNN outperforms in accuracy its best geometric counterpart (AU/CNN) by
about 30 percent while the best SVM solutions are inferior nearly four times.
For F-score the high advantage of raw/CNN over geometric/CNN and geometric/SVM
is observed, as well. We conclude that contrary to CNN based emotion
classifiers, the generalization capability wrt human head pose is for SVM based
emotion classifiers poor.
