@ARTICLE{Jadhav_Nagesh_Multi-model_2021,
 author={Jadhav, Nagesh and Sugandhi, Rekha},
 volume={69},
 number={6},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e138819},
 howpublished={online},
 year={2021},
 abstract={In the domain of affective computing different emotional expressions play an important role. To convey the emotional state of human emotions, facial expressions or visual cues are used as an important and primary cue. The facial expressions convey humans affective state more convincingly than any other cues. With the advancement in the deep learning techniques, the convolutional neural network (CNN) can be used to automatically extract the features from the visual cues; however variable sized and biased datasets are a vital challenge to be dealt with as far as implementation of deep models is concerned. Also, the dataset used for training the model plays a significant role in the retrieved results. In this paper, we have proposed a multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on the visual cues. We have used a CNN and pre-trained ResNet-50 model for the transfer learning. VGGFace model’s weights are used to initialize weights of ResNet50 for fine-tuning the model. The proposed system shows significant improvement in test accuracy in affective state recognition compared to the singleton CNN model developed from scratch or transfer learned model. The proposed methodology is validated on The Karolinska Directed Emotional Faces (KDEF) dataset with 77.85% accuracy. The obtained results are promising compared to the existing state of the art methods.},
 type={Article},
 title={Multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on visual cues},
 URL={http://journals.pan.pl/Content/120733/PDF-MASTER/Z_15_02234_Bpast.No.69(6)_OK.pdf},
 doi={10.24425/bpasts.2021.138819},
 keywords={deep learning, convolution neural network, emotion recognition, transfer learning, late fusion},
}