1 Rapid Hand Posture Recognition Using Adaptive Histogram Template of Skin and Hand Edge Contour Abstract: In this paper, we propose a real-time vision-based hand posture recognition approach, based on appearance- based features of hand. Our approach has three main steps: hand segmentation, feature extraction and posture recognition. For the hand segmentation, we introduce “Adaptive Histogram Template of Skin” which tries to extract histogram of the subject hand by sampling its color and texture. With this template, we can use back projection method to find skin color areas in an image. In the feature extraction step, we extract global hand's features using hand's edge contour, and hand's edge convex hull. The hand can be classified into one of the ten posture classes in the recognition step. Each posture class has a representative template which is used as reference for comparing to subject hand features. This approach is simple and fast enough to provide real-time recognition. Keywords: hand posture recognition, skin detection, Hu invariant moments. 1. Introduction Hand posture recognition is a challenging computer vision and pattern recognition issue [1,2]. The human hand is a complex articulated object consisting of many connected parts and joints. Considering the global hand pose and each finger joint, human hand motion has roughly 27 degree of freedom (DOF) [3]. Nowadays, the majority of human-computer interfaces are mechanical devices such as keyboard and mouse and joysticks. In recent years there has been a growing interest in methods based on computational vision due to its ability to recognise human gestures in a natural way [4]. In the literature of gesture recognition, there are two important definitions need to be cleared: hand posture and hand gestures. A hand posture is defined solely by the static hand configuration and hand location without any movements involved. A hand gesture refers to a sequence of hand postures connected by continuous motions (global hand motion and local finger motion) over a short time span [5]. Vision-based hand gesture recognition techniques can be divided into two categories: appearance-based approaches and 3D hand model-based approaches [6]. Appearance-based approaches use image features to model the visual appearance of the hand and compare these parameters with the extracted image features from the input video. 3D hand model-based approaches rely on a 3D kinematic hand model with considerable degrees of freedom and try to estimate the hand parameters by comparison between the input images and possible 2D appearance projected by the 3D hand model [5]. 1.2 Related Works Hand gesture recognition has been addressed in [7] and [8], but only for counting the number of stretched fingers. Gesture recognition has been also addressed in [9], but instrumented gloves are used, which simplifies the recognition problem. Moment invariants, and chain code histograms methods has been approached in [10,11,12] respectively. A 3D recognition approach requiring a multi-camera, is proposed in [13]. A simpler 3D approach is addressed in [14], which requires a 3D model with more than twenty degrees-of-freedom, as well as a continuous, error-free tracking of the hand (for updating 2D to 3D correspondence). 2. Our Approach In the present work, we focus on hand postures. Furthermore, we use Appearance-based approach because of its simplicity and advantage of real-time performance. Our approach has three main steps: hand segmentation, feature extraction and posture recognition. For the hand segmentation, we introduce “Adaptive Histogram Template of Skin” which tries to extract histogram of the subject hand by sampling its color and texture. With the adaptive histogram template of skin, we can use back-projection method to find skin color areas in an image. In the feature extraction step, we extract global hand's features using hand's edge contour and hand's edge convex hull. From the hand’s contour and the hand’s convex hull, we can calculate a sequence of contour points between two consecutive convex hull vertices. Ghassem Tofighi*, S.Amirhassan Monadjemi**, and Nasser Ghasem-Aghaee*** *University of Isfahan, tofighi@alum.sharif.edu ** University of Isfahan, monadjemi@eng.ui.ac.ir *** University of Isfahan, aghaee@eng.ui.ac.ir 978-1-4244-9708-9/10/$26.00 ©2010 IEEE