The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.
Extracted features may describe not only the motion of hands but also their estimated pose. A review of literature regarding hand pose estimation is provided in @cite_21 . Creation of a gesture model can be performed using multiple approaches including Hidden Markov Models e.g. @cite_22 or Dynamic Bayesian Networks e.g. @cite_25 . For hand gesture recognition, application domains include: sign language recognition e.g. @cite_3 , robotic and computer interaction e.g. @cite_29 , computer games e.g. @cite_32 and virtual reality applications e.g. @cite_25 .
@cite_22 @cite_29 @cite_21 @cite_32 @cite_3 @cite_25
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Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted An intelligent robot requires natural interaction with humans. Visual interpretation of gestures can be useful in accomplishing natural human-robot interaction (HRl). Previous HRI researches were focused on issues such as hand gesture, sign language, and command gesture recognition. However, automatic recognition of whole body gestures is required in order to operate HRI naturally. This can be a challenging problem because describing and modeling meaningful gesture patterns from whole body gestures are complex tasks. This paper presents a new method for spotting and recognizing whole body key gestures at the same time on a mobile robot. Our method is simultaneously used with other HRI approaches such as speech recognition, face recognition, and so forth. In this regard, both of execution speed and recognition performance should be considered. For efficient and natural operation, we used several approaches at each step of gesture recognition; learning and extraction of articulated joint information, representing gesture as a sequence of clusters, spotting and recognizing a gesture with HMM. In addition, we constructed a large gesture database, with which we verified our method. As a result, our method is successfully included and operated in a mobile robot. User interaction is an essential feature in the design of an interactive game. Most existing games receive inputs from users via conventional devices such as keyboard, mouse, joystick and paddle. More recent games make use of infrared beams from user's devices, the stylus from touch screen, or pressure-sensing pads to provide rich contextual sensing and interactions. In this paper, we propose the use of hand gestures as the basis for users to directly interact with game objects that are rendered across a flat plasma or LCD display. It forms a new paradigm of interaction in which the physical movements of hands in the form of hand gestures are coordinated along with the virtual objects in the game. Thus, the user effectively becomes an “input device”. We make use of a low-cost web camera that is mounted over the gaming screen display to provide image-feed to the hand tracking and gesture recognition system, called Germane, which employs the hull-point analysis algorithm for gesture recognition. A working prototype of Germane has been developed to validate its operations on several common gestures. Performance evaluation results of Germane are also presented. This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a precis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets. The recognition of hand gestures is a challenging task for the high degrees of freedom of hand motion. We develop a virtual reality based driving training system of Self-Propelled Gun (SPG). For this system, a DataGlove with 18 sensors is employed to perform some driving tasks such as pressing switches, manipulating steering wheel, changing gears, etc. To accomplish these tasks, some hand gestures must be defined from the DataGlove sensors data. A feedforward neural network can represent an arbitrary functional mapping so it is possible to map raw data directly to the required hand gestures. This paper uses BP neural network to recognize the hand patterns which exist in the raw sensor data of the DataGlove. A pattern set of 300 hand gestures is used to train and test the neural network. The recognition system achieves good performance. It can be effectively used in our virtual reality training system of SPG to perform various manipulating tasks in a more fast, precise, and natural way.