International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-1, November 2019 1161 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: A4489119119/2019©BEIESP DOI: 10.35940/ijitee.A4489.119119 Hand Gesture Recognition using Convexity Defect Caroline El Fiorenza, Sandeep Kumar Barik, Ankit Prajapati, Sagar Mahesh Abstract: Gestures are the simplest way of conveying a message, rather simpler than verbal means. It is the most primitive way of conversation. Gestures can also be the easiest and intuitive way of communicating with a computer, they can be used to communicate or convey information to computers, robots, smart appliances and many other pieces of machinery. It can eliminate the use of mouse and keyboard to some extent. The gestures cited are basically the variable positions as well as orientations of the hand. They can be detected by a simple webcam attached to the computer. The image is first changed into its corresponding RGB values and then to HSV values for better handling and feature recognition. The hand is segregated from the background using feature extraction. Then the values are matched in proximity of the coded values. Then the region of interest is calculated using the concept of convexity and background subtraction. The convex defect helps to define the contour efficiently. This method is invariant for different positions or direction of the gesture. It is able to detect the number of fingers individually and efficiently. Keywords: RGB, HSV, hand gesture, background subtraction, detection, feature extraction, convexity defect. I. INTRODUCTION Gestures have been used since primitive times to convey messages in the simplest of fashions. Simple messages, if not sophisticated were communicated by the variable positions and orientations of the hand and other parts of the body. They can be originated from any bodily movement. The use of gestures as a form of communicating with the digital media such as the PC and other smart devices drives as the motivating force to analyse and record the movements. The system needs to be visually intelligent to capture and understand the movement. This includes multi-disciplinary studies [1]. The gesture recognition system is rapidly developing and is the stepping stone for the advancement in smart appliances as well as IOT enabled devices. The Human Computer Interaction (HCI) [1], is simple and more intuitive as compared to the conventional keyboard and mouse-based input. Gestures are easy to form and remember and easier to produce. This system finds application in simple PCs, smart appliances, PDAs, medical equipment, public accessibilities, offices with digital media, entertainment purposes etc. The proposed system can be dissociated into the following modules; sample image capturing, image pre-processing, feature analysis, parameters extraction, classification and recognition [3]. Revised Manuscript Received on November 06, 2019. * Correspondence Author Ms. Caroline El Fiorenza, Assistant Professor (O.G.), Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai Mr. Ankit Prajapati, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai Mr. Sandeep Kumar Barik, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai Mr. Sagar Mahesh, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai The paper is organised as follows, Literature survey, materials and methods, features extraction, results and discussion and at last the conclusion. II. LITERATURE SURVEY In one of the papers [1] the main contribution is to introduce a forward movement finding strategy that performs the separation and detection of hand signals all the time. A stochastic approach is suggested to design a non- motion process using CRFs model without data planning. This system could efficiently identify signals and non- motion models in tandem with forward spotting program. Often, consider the time delay problems between the detection and identification undertakings. The biggest advantage of this method is that it improves efficiency and also allows us to deal on complex issues that are abstracted from the consumer and operate in the background, rendering it more efficient and easier as well. In another paper [2] the target of this paper is to plan a hand motion acknowledgment framework that works continuously and perceived manipulative hand signals. The motions that are utilized in this acknowledgment framework have particular significance. Every single one of these signals speaks to a specific activity. This paper worries to plan a component extraction technique that is invariant overturn, scaling, interpretation, and direction-dependent on minute element extraction strategy with the goal that the framework can perceive hand motions caught in various point or direction or size. A major demerit would be that the proposed method is susceptible to errors, especially in shapes like square and circular. Human hand following [3], it requires predefining a gathering of signal accumulation, from which getting the main motion descriptor. In view of the mapping from picture highlight space to signal space, we can evaluate legitimately motion. By and large, such picture highlights incorporate dab, line, corner or surface zone and so forth. The use of this strategy needs setting up the mapping connection between picture qualities and human hand act. Nonetheless, close by moving procedure, hand's expressive sharp changes make the mapping unquestionably profoundly non-direct. This technique doesn't necessities ascertaining 3D motions, rather, it requires learning and preparing in ample datasets which portray any conceivable signal. Advantage of this would be objects dominating the image will be represented as orientation histograms. [4] We predominantly present the pre-processing steps of motion pictures before we feed them into the DCNN. To a limited extent A, we acquaint a shading offset calculation with expelling the obstruction brought about by shaky light sources and different components. Part B presents the signal zone extraction technique dependent on Gaussian blend model. Part C presents reproduction procedure of the entire