Information Measure Ratio Based Real Time Approach for Hand Region Segmentation With a Focus on Gesture Recognition Ayan Chaki Innovation Lab TATA Consultancy Services Ltd. Kolkata, India ayan.chaki@tcs.com Pragya Jain Computer Science Department IEM Kolkata, India pragya.jain15@gmail.com Rohit Kumar Gupta Innovation Lab TATA Consultancy Services Ltd. Kolkata, India rohit3.gupta@tcs.com Abstract—This paper presents an efficient and real time novel approach for hand region segmentation with an aim to achieve hand gesture recognition under varying illumination. The overall methodology is a two step process: (a) to achieve the segmentation of the hand from the complex background and (b) to recognize the hand gesture efficiently and accurately. Hand segmentation is achieved using block based picture information ratio and recognition is done using Principal Component Analysis (PCA). In the experimental results four basic hand gestures have been considered which were recognized consistently in complex but near constant background and varying illumination. The prototype has been developed in X86, tested with live videos captured by low cost webcams . It performs with 98% accuracy in real time. Keywords- hand gesture; Information Measure Ratio; PCA; segmentaion; human computer interface I. INTRODUCTION Gestures have always played an important role in human life. In the past two decades or so there has been a huge advancement in the field of vision based action recognition. One major challenge to achieve higher degree of accuracy for hand gesture recognition lies in segmenting the hand region under varying illumination and complex background [1]. The main purpose of developing such a system lies in the fact that gesture recognition has implementations in almost all fields. There are multiple needs for gesture recognition and it is note worthy to discuss the basics and need of gesture recognition. Gesture recognition has been used in many places from motion analysis to machine learning. Apart from this it serves many purposes from in-flight entertainment to medicinal uses. Different systems have been developed to implement gesture recognition in different ways. On one side we have the vision based action recognition systems which take the help of one or more cameras to get a 3D hand model for higher accuracy While on the other side the appearance based system exists which uses only a single camera and makes a set of templates using the training data fed to the machine from before. In this paper, real time hand region segmentation with an aim to achieve gesture recognition has been proposed. The proposed system is developed on an efficient and novel approach to segment the hand from the complex background and applying Principal Component Analysis (PCA) is proposed. It is done using a single camera with the aim of having an algorithm which is fast, simple and accurate. The simplicity of this system allows it to be used on every desktop computer. PCA has a unique property of reducing the dimensionality of the picture and hence making the process cheaper in terms of time which is a huge advantage in real-time systems. This paper has been divided into sections with Section 2 containing information of the previous work that has been done in this field, Section 3 containing the main implementation of the algorithm, Section 4 containing the results and discussions and Section 5 concluding the paper. II. STATE OF THE ART Gesture based recognition can be thought of as a classification problem where as an input a set of images are given and the desired actions are obtained as the output. Much work has been done in this field with each having a unique approach but still trying to achieve the same goal. Lu and Little [2] have used a Hybrid Hidden Markov Model (HMM) with two first order processes which allowed them to combine tracking and recognition both in a single frame. They introduced dependencies between the current and previous action to ensure better recognition. It is to be mentioned that one of the best approaches is implementing the Histograms of Oriented Gradients (HoG). This fact has been experimentally proved by Dalal and Triggs [3]. They have used a linear SVM (Support Vector Machine) as a baseline classifier to achieve speed and simplicity. An integration of cascade-of-rejecters approach with HoG to achieve a faster human detection system has been done by Zhu et al. [4]. Using an integral image approach with variable size blocks allowed them achieve this aim. After getting the blocks they used the AdaBoost to get the best suited blocks and used these blocks to construct the rejector-based cascade. Wei-Lwun et al [10] also 2011 Second International Conference on Intelligent Systems, Modelling and Simulation 978-0-7695-4336-9/11 $26.00 © 2011 IEEE DOI 10.1109/ISMS.2011.36 172 2011 Second International Conference on Intelligent Systems, Modelling and Simulation 978-0-7695-4336-9/11 $26.00 © 2011 IEEE DOI 10.1109/ISMS.2011.36 172