RT Journal Article
A1 Hemant Kumar Meena
A1 Kamalesh Kumar Sharma
A1 Shiv Dutt Joshi

PB iet
T1 Facial expression recognition using the spectral graph wavelet
JN IET Signal Processing
VO 13
IS 2
SP 224
OP 229
AB The authors propose a method to recognise facial expressions based on graph signal processing (GSP) techniques. Facial expressions are characterised by local patterns in the facial regions such as eyes, lips etc. and interrelationships among them. A facial expression recognition algorithm needs to capture these variations in the facial regions at the local level and the interrelationships of these regions at the global level. Hence, in the authors’ opinion, GSP seems to be an appropriate tool for the purpose. In this study, a novel method is presented which makes use of graph signals to represent the facial regions. They leverage spectral graph wavelet transform to extract information for creating the feature descriptor. Here, different types of two-channel and three-channel filter banks have been used by setting the weights of their channels for finding the optimum performance of the recognition rate. Through simulation studies, it is observed that the use of Abspline filter bank provides the best result. The experimental investigations on the extended Cohn–Kanade (CK+) and the JAFFE datasets have been carried out and the results confirm the effectiveness of the proposed method in recognition rate improvement.
K1 CK+ dataset
K1 graph signal processing techniques
K1 facial regions
K1 extended Cohn-Kanade dataset
K1 facial expression recognition algorithm
K1 Abspline filter bank
K1 three-channel filter banks
K1 local patterns
K1 spectral graph wavelet transform
K1 GSP techniques
K1 two-channel filter banks
K1 feature descriptor
K1 JAFFE dataset
K1 recognition rate improvement
DO https://doi.org/10.1049/iet-spr.2018.5087
UL https://digital-library.theiet.org/;jsessionid=1143walit52qa.x-iet-live-01content/journals/10.1049/iet-spr.2018.5087
LA English
SN 1751-9675
YR 2019
OL EN