TY  - JOUR
T1  - Taylor-Swarm Ganet: Learning Illumination Invariant Feature Descriptor For Facial
Expression Recognition using Deep Generative Adversarial Network
AU - Gavade, Priyanka A. AU - S. Bhat, Vandana AU - Pujari, Jagadeesh 
JO  - Journal of Engineering and Applied Sciences
VL  - 16
IS  - 6
SP  - 208
EP  - 221
PY  - 2021
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2021.208.221
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2021.208.221
KW  - Facial Expression Recognition (FER)
KW  -Deep Generative Adversarial Network (GAN)
KW  -video March 23
KW  -compression
KW  -Taylor series
KW  -Viola Jones algorithm
AB  - Facial expression is the nonverbal way to
express the human intensions and emotions. Facial
Expression Recognition (FER) intends to understand and
analyze the facial behavior of humans such that it has
become an active research area in the field of pattern
recognition, artificial intelligence and computer vision.
Various FER methods are developed for classifying the
facial expression in video sequences but to extract the
discriminative video features from the facial expression
images results a key challenging issue in FER system.
Hence, an effective FER method is designed using
proposed Taylor-Chicken Swarm Optimization-based
Deep Generative Adversarial Network (Taylor-CSO
based Deep GAN) for the recognition of facial emotions.
However, the proposed method named Taylor-CSO is
derived by the integration of Taylor series with Chicken
Swarm Optimization (CSO), respectively. The process of
Illuminant Invariant Local Binary Pattern (IILBP) is made
by employing the LBP descriptor to the facial object.
Based on the feature matrix, the process of FER is
accomplished using Deep GAN. However, the proposed
approach achieved the accuracy, precision and recall of
0.8846, 0.8996 and 0.8952 with respect to training data.
ER  -