# Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
## 5 Conclusions  
Real time detection of any kind of activity that is suspicious in nature is difficult to identify without any actual interaction with the subject or suspect. Reading the face of a person in real time is a challenging task. With the help of compact and portable devices, it becomes easy for the majority of organizations to understand the behavior of their employees and resolve some of the minor and major issues at an early stage. To achieve that, a framework has been tested and proposed that can be implemented in any organization to understand employee behavior. The proposed framework is a cost-effective and compact alternative over all those heavy and bulky systems that are difficult to implement in real time. The system has been tested for 20 different people with all 7 emotions, and out of total 120 images, 110 images were identified with correct emotions in real time. The proposed framework has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time as compared to other networks. Facial expression representation plays an important role in facial expression recognition. It can be viewed as generating good features for describing the appearance, structure, and motion of facial expressions. More specifically, facial expression features attempt to effectively describe the facial muscle or facial motion for static or dynamic facial images. Numerous works have already done this and, although different proposed methods for \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Angry** & **0.68** & 0.01 & 0.05 & 0.04 & 0.08 & 0.03 & 0.10 \\  & **Disgust** & 0.47 & **0.54** & 0.02 & 0.02 & 0.00 & 0.04 & 0.02 \\  & **Fear** & 0.20 & 0.00 & **0.50** & 0.03 & 0.15 & 0.12 & 0.12 \\  & **Happy** & 0.03 & 0.00 & 0.01 & **0.89** & 0.01 & 0.03 & 0.03 \\  & **Sad** & 0.14 & 0.07 & 0.05 & 0.06 & **0.45** & 0.02 & 0.24 \\  & **Surprise** & 0.04 & 0.01 & 0.07 & 0.03 & 0.01 & **0.81** & 0.02 \\  & **Neutral** & 0.08 & 0.01 & 0.03 & 0.06 & 0.08 & 0.02 & **0.73** \\  & & **Angry** & **Disgust** & **Fear** & **Happy** & **Sad** & **Surprise** & **Neutral** \\  & & & & **Predicted Label** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion Matrix of the testing dataset after data augmentation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Research**} & **Objective** & **Hardware Based** & \multirow{2}{*}{**Cloud Server**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Accuracy**} \\  & & & **Device** & & & \\ \hline \multirow{2}{*}{[26]} & Face emotion & & & & & \\  & recognition & No & No & Hybrid CNN-RNN & 94.91 \\ \hline \multirow{2}{*}{[27]} & Facial expression & & & & \\  & recognition & No & No & CNN & NA \\ \hline \multirow{2}{*}{[28]} & Emotional & & & & \\  & Recognition in the & & Yes & No & CNN & NA \\ \hline \multirow{4}{*}{[29]} & Facial Expression & & & & \\  & Emotion Detection & & & & \\  & board & & & & \\ \hline \multirow{2}{*}{Proposed} & Facial emotion \& & & & & \\  & detection & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of proposed edge device with previous studies. facial expression recognition have achieved good results, there remain different problems that need to be addressed by the research community. The most important one is face variability in a single person. There are many factors that can cause two pictures from the same person to look totally different, such as light, face expression, or occlusion. Another problem to be taken into account is the environment. Except in controlled scenarios, face pictures have very different backgrounds, which can make the problem of face recognition more difficult. To address this issue, many of the most successful systems focus on treating the face alone, discarding all the surroundings. Smart meeting, video conferencing, and visual surveillance are some of the real-world applications that require a facial expression recognition system that works adequately on low resolution images.