000			a
999			_c22703 _d22703
003			OSt
005			20250424094408.0
008			250424b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_925966 _aSudha, S. S.
245			_aDeep q-network (DQN) partialocclusion segmentation and Backtracking search optimization algorithm (BSOA) with optical Flow reconstruction for facial expression emotion recognition
250			_aVol.15(2), Oct
260			_aChennai _bICT Academy _c2024
300			_a3473-3481p.
520			_aVideo facial expression recognition (FER) has garnered a lot of attention recently and is helpful for several applications. Although many algorithms demonstrate impressive performance in a controlled environment without occlusion, identification in the presence of partial facial occlusion remains a challenging issue. Solutions based on reconstructing the obscured area of the face have been suggested as a way to deal with occlusions. These options mostly rely on the face’s shape or texture. Nonetheless, the resemblance in facial expressions among individuals appears to be a valuable advantage for the reconstruction. For semantic segmentation based on occlusions, Reinforcement Learning (RL) is introduced as the initial stage. From a pool of unlabeled data, an agent learns a policy to choose a subset of tiny informative image patches to be tagged instead of full images. In the second stage, a trained Backtracking Search Algorithm (BSA) is used to rebuild optical flows that have been distorted by the occlusion. On obtaining optical flows estimated from occluded facial frames, AEs restore optical flows of occluded regions. These recovered optical flows become inputs to anticipate classes f expressions. Optical flux reconstructions then classify stages. This study evaluates classification model’s performances for face expression identification based on Very Deep Convolution Networks (VGGNet). Furthermore, it produces more accurate confusion matrices and proposes approaches for the KMU- FED and CK+ databases, respectively. The results are evaluated using metrics including recall, f-measure, accuracy, and precision.
650		0	_94622 _aComputer Engineering
700			_925967 _aSuganya, S. S.
773	0		_dChennai ICT Academy _tICTACT Journal on Soft Computing (IJSC)
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_12_3556_3566.pdf _yClick here
942			_2ddc _cAR