Weight-Adapted Convolution Neural Network for Facial Expression Recognition in Humanâ??Robot Interaction
The weight-adapted convolution neural network
(WACNN) is proposed to extract discriminative expression representations
for recognizing facial expression. It aims to make
good use of the convolution neural networkâ??s (CNNâ??s) potential
performance in avoiding local optima and speeding up convergence
by the hybrid genetic algorithm (HGA) with optimal initial
population, in such a way that it realizes deep and global emotion
understanding in humanâ??robot interaction. Moreover, the idea
of novelty search is introduced to solve the deception problem
in the HGA, which can expend the search space to help genetic
algorithm jump out of local optimum and optimize large-scale
parameters. In the proposal, the facial expression image preprocessing
is conducted first, then the low-level expression features
are extracted by using a principal component analysis. Finally,
the high-level expression semantic features are extracted and recognized
by WACNN which is optimized by HGA. In order to
evaluate the effectiveness of WACNN, experiments on JAFFE,
CK+, and static facial expressions in the wild 2.0 databases
are carried out by using k-fold cross validation, and experimental
results show the recognition accuracies of the proposal
are superior to that of the state-of-the-art, such as local directional
ternary pattern and weighted mixture deep neural network
(DNN), which aim to extract discriminative and are the DNNbased
methods. Moreover, recognition accuracies of the proposal
are also higher than the deep CNN without HGA, which indicates
that the proposal has better global optimization ability.
Meanwhile, preliminary application experiments are also carried
out by using the proposed algorithm on the emotional social robot
system, where nine volunteers and two-wheeled robots experience
the scenario of emotion understanding. Application results indicate
that the wheeled robots can recognize basic expressions, such
as happy, surprise, and so on.
KeyWords
Accelerated gradient descent (AGD) convolution
neural network (CNN) facial expression recognition
(FER) genetic algorithm (GA)
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