## 5 Conclusions  
A novel method for facial expression recognition systems has been proposed in this work. The objective of the proposed system is to predict the seven basic types of expressions on the human face. The applications of this proposed system have been well described and demonstrated in the diversified fields of e-Healthcare, social IoT, emotion AI, and cognitive AI. The implementation of the proposed system has three components. In the first component, an image preprocessing task has been performed where a face region was extracted from a body silhouette image using the facial landmark points. Then, in the second component, from the extracted face region, the multi-resolution images were considered. The convolutional neural network architectures have been proposed for each resolution of the images. Here, the images undergo the CNN architectures and are classified into seven basic facial expression classes based on learning the parameters of CNN models. To enhance the performance of the recognition system and better handle the challenging issues of the facial expression recognition system, some advanced techniques such as image augmentation, progressive image resizing, transfer-learning, and fine-tuning of parameters were employed in the third component. Finally, fusion methods were applied to the best performance of the different CNN models to achieve a better performance than the existing state-of-the-art methods. Extensive experimentation has been performed using four benchmark databases, namely KDEF, GENKI-4k, CK+, and SFEW, and the performance of the proposed system has been compared with some existing methods concerning each database. The comparison of the performance of the proposed method with the competing methods shows the superiority of the proposed system.  
Original Draft Preparation, Methodology, S.U.; Investigation, Formal Analysis, S.H.; Supervision, Funding Acquisition, V.A.; Conceptualization, Validation, R.K.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Dr. [PERSON], Professor in Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469-0232.  
The ethics committee or institutional review board approval is not required for this manuscript. This research respects all the sentiments, dignity, and intrinsic values of animals or humans.  
Not applicable.  
In this manuscript, the employed datasets have been taken with license agreements from the corresponding institutions with proper channels.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Accuracy (\%)** & **Remarks** \\ \hline Vgg16 [67] & 24.78 & Images used (700), expression class (7), train (346)/test (354) \\ \hline ResNet50 [68] & 24.98 & Images used (700), expression class (7), train (346)/test (354) \\ \hline Inception-v3 [69] & 29.52 & Images used (700), expression class (7), train (346)/test (354) \\ \hline [PERSON] et al. [74] & 26.14 & Images used (700), expression class (7), train (346)/test (354) \\ \hline
**Proposed** & **36.79** & Images used (700), expression class (7), Train (346)/Test (354) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison of the proposed FERS for the SFEW database.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Accuracy (\%)** & **Remarks** \\ \hline ResNet50 [68] & 91.87 & Images used (981), expression class (7), train/test split \\ \hline Inception-v3 [69] & 94.07 & Images used (981), expression class (7), train/test split \\ \hline [PERSON] et al. [73] & 94.67 & Images used (510), expression class (7), k-fold cross-validation \\ \hline
**Proposed** & **96.81** & Images used (981), proposed CNN for seven expression classes \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison of the proposed FERS for the CK+ database.