# Multi-Class Confidence Detection Using Deep Learning Approach
## 1 Introduction  
Human-Computer Interaction (HCI) improves the relationship between humans and machines by promoting the recognition of dynamic human gestures to extract useful insights. Gesture recognition and human interaction with a computer are now becoming trending research topics, as researchers focus on recognizing and analyzing their appearance features [1]. Hand gestures are important in HCI because they are a natural mode of communication and can also be utilized for controlling devices [2, 3]. More commonly, two possible ways of human interaction with surrounding objects or others are categorized as (a) glove-based with integrated sensors and (b) vision-based through detection and recognition of hand movement to classify gestures. The application of human gesture movement applies to different areas of assistance, including smart surveillance, vision-based classifiers, sign language, virtual reality, robotics, and entertainment [4]. Continuous movements of the human body are known as gestures. Manually, a person can recognize these gestures with minimal effort, but it becomes challenging when recognition has to be conducted by a machine. Human gestures include physical movements of body parts such as fingers, hands, arms, neck, and eyes [5]. In Human-Machine Interaction (HMI), hand motions are considered the most valuable and intuitive instrument compared to other body parts. In general, gestures are divided into two types: static gestures and dynamic gestures. An immovable configuration, such as a single image frame of a pose, is categorized as a static gesture. In contrast, a dynamic gesture is a sequence of images, i.e., a video of postures concerning a particular time frame. Overall, static gestures utilize a unit frame that holds slight information, so the computational cost is also tiny. On the contrary, dynamic gestures hold important information, so the computational complexity is also enormous. They are preferable for real-time applications as they contain time references and video sequences. In this research, we categorized each class based on hand gestures. For example, if a person keeps rubbing their hands and fingers, it means that person is uncomfortable. Similarly, if a person joins their hands in a video frame at some point and keeps repeating the same movement in the next frame, it means that the person is cooperative. If during an interview, a person keeps moving their hands while explaining, it means that person is confused or unconfident about their stance. If a person is using hand movements at regular intervals, it means the person is confident in their stance. Strong and decisive movements, e.g., raised fist or firm hand movements, are counted as confident gestures. In contrast, unconfident or confused gestures are more hesitant; for example, movements might be slow, haphazard, and less coordinated. Therefore, it is important to consider the full range of hand gesture cues to get a specific context [6]. Researchers have worked to propose different machine-learning methods for the detection and recognition of human hand gestures. In the scientific field of gesture recognition, hand motions include movements of thumbs, palms, and fingers for recognition, identification, and detection of the gestures [7]. Due to the small size of hands compared to other body parts, their estimation and detection are difficult, yielding excessive recognition complexity and frequent self-occlusions. Effective analysis of hand gestures is complex, given the higher performance cost. Many problems such as background, occlusion, illumination changes, color sensitivity, and light variations have been encountered in the approaches used for gesture analysis, particularly from 2-D video frames captured using digital cameras in the past few decades. In traditional approaches, machine learning algorithms were used to conduct the recognition activity.