Hand gesture is one of the method used in sign language for non-verbal communication. It is most commonly used by deaf & dumb people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems has been developed by many makers around the world but they are neither flexible nor cost-effective for the end users. Hence in this paper introduced software which presents a system prototype that is able to automatically recognize sign language to help deaf and dumb people to communicate more effectively with each other or normal people. Pattern recognition and Gesture recognition are the developing fields of research. Being a significant part in nonverbal communication hand gestures are playing key role in our daily life. Hand Gesture recognition system provides us an innovative, natural, user friendly way of communication with the computer which is more familiar to the human beings. By considering in mind the similarities of human hand shape with four fingers and one thumb, the software aims to present a real time system for recognition of hand gesture on basis of detection of some shape based features like orientation, Centre of mass centroid, fingers status, thumb in positions of raised or folded fingers of hand

Sign language recognition using image based hand gesture recognition techniques

Fast and robust occluded face detection in ATM surveillance

Gesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method; the ROI in the scene is thus extracted by the local neighbor method. In this way, the hand can be identified quickly and accurately in complex scenes and different distances. Furthermore, the convex hull detection algorithm is used to identify the positioning of fingertips in ROI, so that the fingertips can be identified and located accurately. The experimental results show that the hand position can be obtained quickly and accurately in the complex background by using the improved method, the real-time recognition distance interval can be reached by 0.5 m to 2.0 m, and the fingertip detection rates can be reached 98.5% in average. Moreover, the gesture recognition rates are more than 96% by the convex hull detection algorithm. It can be thus concluded that the proposed method achieves good performance of hand detection and positioning at different distances.

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Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information

Fingertip tracking plays an increasingly important role in augmented reality and virtual-reality applications. However, existing approaches (either continuous-detection-based or separated-detection-tracking-based methods) cannot effectively learn temporal-spatial information or even separate fingertip tracking into unrelated stages, which causes poor real-time performance and incomplete tracking continuity. Moreover, due to the need for high-cost devices, the high degrees of freedom of the hand, and subtle differences among fingers, fingertip tracking remains a challenging task. To address these problems, we propose a novel tracking-combined-with-detection approach for vision-based fingertip tracking. By adopting clustering and geometric constraint analysis, we develop a curvature points clustering method for fingertip detection. Then, by exploiting the identified fingertip points for motion estimation with bidirectional optical flows and temporal-spatial probability calculation, the tracking stage is effectively integrated with the detection stage. To accurately locate the fingertip, we represent the fingertip model with a perceptual hash sequence and locate the fingertip by searching for the best-matching region. Extensive experimental results show the superiority of the proposed algorithm to commonly used and state-of-the-art methods and demonstrate its effectiveness and practicability.

Vision-Based Fingertip Tracking Utilizing Curvature Points Clustering and Hash Model Representation

Kinect v2 sensor does not provide depth information and skeletal traction function in near distance from the sensor. That is why many researches, to recognize hand gestures, are focused on the skeletal tracking only inside the range of detection. This paper proposes a method which can recognize hand gestures in the distance less than 0.5 meter without conventional skeletal tracking when Kinect v2 sensor is used. The proposed method does not use the information of depth sensor and infrared sensor, but detect hand area and count the number of isolated areas which are generated by drawing a circle in the center of the hand area. This method introduces new detectable gestures without high cost, so that it can be a substitute for the existing mouse-movement controlling and dynamic gesture recognition method such as clicking a mouse, clicking and dragging, rotating an image with two hands, and scaling an image with two hands in near distance. The gestures are appropriate for the user interface of smart devices which employ the interactions based on hand gestures in near distance.

Hand Gesture Recognition for Kinect v2 Sensor in the Near Distance Where Depth Data Are Not Provided

Gestures are important for communicating information among the human. Nowadays new technologies of Human Computer Interaction (HCI) are being developed to deliver user's command to the robots. Users can interact with machines through hand, head, facial expressions, voice and touch. The objective of this paper is to use one of the important modes of interaction i.e. hand gestures to control the robot or for offices and household applications. Hand gesture detection algorithms are based on various machine learning methods such as neural networks, support vector machine, and Adaptive Boosting (AdaBoost). Among these methods, AdaBoost based hand-pose detectors are trained with a reduced Haar-like feature set to make the detector robust. The corresponding context-free grammar based proposed method gives effective real time performance with great accuracy and robustness for more than four hand gestures. Rectangles are creating some problem due to that we have also implement the alternate representation method for same gestures i.e. fingertip detection using convex hull algorithm.

Ruchi Manish Gurav

Papers

Real time finger tracking and contour detection for gesture recognition using OpenCV