# Multi-Class Confidence Detection Using Deep Learning Approach
## 1 Introduction
For this process, the user had to manually design significant features, following a framework for solving problems such as hand variation in terms of varying size and lighting states. In the hand-crafted approach, feature extraction is carried out in a problem-oriented way. To create hand-crafted features in computing, the right trade-off between performance and accuracy is required. The emergence of deep learning and modern developments in computing and data resources, along with powerful hardware, has led to a paradigm shift in the field of artificial intelligence (AI) and computer vision. Recently, deep-learning-based models have been able to extract helpful information from videos implicitly, without any dependencies on external algorithms for data processing. Remarkable findings are now being obtained in various activities, including posture and hand gesture recognition. Existing works on video content put together activity acknowledgment zeroed in primarily with respect to adjusting hand-created highlights to perform characterization [8]. This includes an underlying component recognition stage, which is then changed over completely to fixed-length highlight descriptors, trailed by characterization utilizing standard classifiers like Support Vector Machines (SVM). In any case, the analysis indicates that the best-performing highlight descriptor is dataset-dependent, and there is no general hand-designed feature that beats all others [9]. Hence, it is exceptionally helpful to learn dataset explicit low-level, mid-level, and significant-level elements, in regulated, unaided, or semi-directed settings. Since the recent re-emergence of neural networks summonedby [PERSON] and others, profound brain structures have become a viable methodology for extricating undeniable level highlights from information [10]. Over the last couple of years, profound counterfeit brain networks have given us cutting-edge results on various machine vision-related errands; for example, object detection, classification, and recognition [11], and localization, image and video segmentation, processing and captioning [12; 13]. Primary signal situated data sets proposed for the ChaLearn 2013 Multi-Modular Gesture Recognition Challenge contained an adequate measure of deep learning policy Test Readiness for profound learning techniques [14]. Currently, automation has become a common task, thriving in the ever-changing era of technology. The unique objectives of the proposed research are as follows:  
* One of the objectives of the proposed research is to design and construct a system to analyze the profile of someone who communicates by gestures. Mainly, we focus on video sequences of moving. * The proposed architecture is trained and tested on a dataset of video clips collected and extracted from Web resources like YouTube. * Another critical objective of our approach is the design of a model to integrate different Machine learning techniques like Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM) and optimize the performance of the recognition of hand gestures. The main contribution of the proposed framework is that it facilitates to analyze and recognize gestures while detecting hand movement. The collected dataset is taken from open-source videos of the appropriate subjects, such as criminal investigations and academic and job interviews that contain gestures relevant to multi-labeled efficacy classes. To detect these postures/movements, the system captures the video and extracts the frames containing hands with hand detection techniques, e.g., haar cascade. These extracted image frames from video streams are then processed to separate the hands' boxes, i.e., Region of Interest (ROI), and resize the image frames containing hands only. These images are then processed further for feature extraction using a convolutional neural network trained on a collected dataset containing labeled image frames for relevant categories.