# Multi-Class Confidence Detection Using Deep Learning Approach
## 2 Related Work
Another approach for customizing temporal sequences is by using recurrent neural networks (RNNs) placed after the CNN's last fully connected layer. The RNN can capture long-range dependencies along with temporal structure modeling. In each time slot, it shares different parameters across the network. One of the models presented in [33] for learning spatio-temporal hierarchical features from videos works by combining deep learning algorithms, i.e., CNN, BLSTM-RNN, and PCA (Principle Component Analysis), but this model is not robust. Therefore, pre-processing ofdata is required when providing real-world data that contains noise. A good model should be robust, i.e., its output and predictions should remain consistent to some extent, even if there is a sudden change in input. Thus, robustness is significant and has been ignored in past work [34; 35]. [PERSON] et al. [36] utilized the CNN-LSTM network to successfully learn and analyze complex features that vary with duration. This combination learns time-related features of each gesture to accurately classify [PERSON]'s stroke phase. To effectively visualize the feature maps for these features, it employs a deconvolution process to activate the original pixels of images. A lot of work has previously been conducted on the detection and recognition of human body gestures. However, there is a need to identify hand gestures and their psychological linkage leading to personality, behavior, and actions. Table 1 summarizes existing work presented by researchers from the earliest computation and body analysis till the advancement of current techniques. Researchers begin studying the area of human gesture recognition by identifying body skeletons and movements in a real-time environment. The challenges arise due to skeletal identification, which can be overcome by analyzing the depth of video frames or images using pre-trained and simple neural networks that enhance the factor of human-computer interaction in a more efficient way. The aim of this research is to provide the classification of human gestures based on their hand position and movement from video clips using CNN and LSTM to analyze their behavior. For this purpose, we obtained regions of interest containing hands cropped from the image frame for extraction of hand position features. These features are then classified into four categories of behavior using LSTM that classify the feature measures from customized CNN. This is the most important component in human-machine interaction, and it is also the most complex one since it is hard to read someone's emotions. It can help in different fields like psychology to determine the body language of a person in a criminal investigation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Ref.** & **Type of Camera** & **Methods** & **Algorithm** & **Results** & **Application Area** \\ \hline \multirow{8}{*}{Skeleton Extraction} & [37] & Kinetic Camera & \begin{tabular}{c} Euclidean \\ distance and \\ geodesic distance \\ \end{tabular} & Extract Skeleton & \begin{tabular}{c} Real-time Hand \\ Tracking \\ \end{tabular} \\  & [38] & RGB Video & Laplacian-based & Skeleton & \begin{tabular}{c} Gesture \\ Recognition and \\ Sign Language \\ \end{tabular} \\  & [39] & Real sense depth & \begin{tabular}{c} Analysis of Depth \\ and Skeleton \\ Extraction \\ \end{tabular} & SVM with Linear & 88.24\% and & Hand Gesture \\  & [40] & Kinect V2 & \begin{tabular}{c} Analysis of Depth \\ Metadata \\ YUV and \\ CAMShift \\ \end{tabular} & SVM & 95.42\% & \begin{tabular}{c} Hand Gesture \\ Recognition \\ \end{tabular} \\  & [41] & Digital Camera & \begin{tabular}{c} Algorithm for \\ skin and \\ movement \\ feature extraction \\ \end{tabular} & Naive Bayes & 97.64\% & \
\begin{tabular}{c} Human and \\ Machine \\ Interaction \\ \end{tabular} \\  & &
\begin{tabular}{c} Framework \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Related Studies.