TY  - JOUR
T1  - A Study on Emotional Identification Using Facial Electromyogram Signals and Neural Networks
AU - Charlyn Pushpa Latha, G. AU - Mohana Priya, M. 
JO  - International Journal of Soft Computing
VL  - 11
IS  - 6
SP  - 437
EP  - 443
PY  - 2016
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2016.437.443
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2016.437.443
KW  - cascade neural network
KW  -feed forward neural network
KW  -elman neural network
KW  -statistical features
KW  -Facial electromyography
KW  -layered recurrent neural network
AB  - This study attempts to state that statistical signal processing treats signals as stochastic processes.
It deals with the statistical properties to process signals and extract significant features. Being a versatile feature
extraction method, it is also used in different areas such as natural language processing, bio-signal processing
and sonar. In this research, it has been examined that the Facial Electromyography signals (FEMG) are
processed by applying the statistical features in order to extract features for categorizing six emotions namely,
happy, fear, neutral, sad, disgust and anger. Twenty subjects have taken part in this experimental study. The
statistical features namely, kurtosis, skewness, moment, range, median absolute deviation and mean have been
used to derive the significant features. Six emotions have been identified by applying the statistical features
as input to neural network models. There are four neural network models namely, Cascade network, Elman
network, Layered recurrent network and feed forward network have been used and compared to identify an
efficient network for emotional identification. The performances of the networks in identifying the six emotions
were in the range of 87.56-98.33%.
ER  -