Hand gestures recognition (HGR) is one of the main areas of research for the engineers, scientists and bioinformatics. HGR is the natural way of Human Machine interaction and today many researchers in the academia and industry are working on different application to make interactions more easy, natural and convenient without wearing any extra device. HGR can be applied from games control to vision enabled robot control, from virtual reality to smart home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the intelligent approaches including soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms etc. The methods in the preprocessing of image for segmentation and hand image construction also taken into study. Most researchers used fingertips for hand detection in appearance based modeling. Finally the comparison of results given by different researchers is also presented.

Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey

Hand gestures recognition is the natural way of Human Machine interaction and today many researchers in the academia and industry are interested in this direction. It enables human being to interact with machine very easily and conveniently without wearing any extra device. It can be applied from sign language recognition to robot control and from virtual reality to intelligent home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms, etc. We also described hand detection methods in the preprocessed image for detecting the hand image. Most researchers used fingertips for hand detection in appearance based modeling. Finally we are comparing results given by different researchers after their implementation.

A Survey on Hand Gesture Recognition in Context of Soft Computing

Hand gesture recognition (HGR) from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of the human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of keyframes of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatiotemporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient and local binary pattern are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D HGR. We also achieve competitive results on NTU action dataset.

Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps

A novel method to calculate the bended finger’s angle has presented here which could be used to control the electro-mechanical robotic hand. It is assumed that the robotic hand has the human hand like joints and same number of degree of freedom as human hand. In many applications an equipment like human hand is needed, to do the same kind of operation like human do. These days it is easy to make the electro-mechanical robotic hand which has five fingers and same joint but it is not easily controllable as the human hand for accurate work. In our method the hand gesture will be interpreted for controlling the robotic hand. The angles for all the fingers will be calculated and that could be further passed to the robotic hand for controlling its finger. User would perform gesture according to the action as he wants to be done by robotic hand. Here finger positions are detected using geometric modeling of hand in the extracted image Region of interest cropping from the image made the algorithm faster.

A Vision based Geometrical Method to find Fingers Positions in Real Time Hand Gesture Recognition

In the last few years gesture recognition and gesture-based human computer interaction has gained a significant amount of popularity amongst researchers all over the world. It has a number of applications ranging from security to entertainment. Gesture recognition is a form of biometric identification that relies on the data acquired from the gesture depicted by an individual. This data, which can be either two-dimensional or three-dimensional, is compared against a database of individuals or is compared with respective thresholds based on the way of solving the riddle. In this paper, a novel method for angle calculation of both hands' bended fingers is discussed and its application to a robotic hand control is presented. For the first time, such a study has been conducted in the area of natural computing for calculating angles without using any wired equipment, colors, marker or any device. The system deploys a simple camera and captures images. The pre-processing and segmentation of the region of interest is performed in a HSV color space and a binary format respectively. The technique presented in this paper requires no training for the user to perform the task.

Both Hands' Fingers' Angle Calculation from Live Video

In this paper we have described a real-time technique for detecting the presence of a hand, and determining hand gestures. The algorithm consists of two steps, the first is to location the hand area and eliminate noise, then seek for angles of the fingers and location of fingertip. With these features, the correct recognition of each fingerspell is achieved. According to the recognition results of difference illumination, the algorithm has a good performance

A Real-time Gesture Recognition Algorithm on Video Surveillance

In order to extract the gait sequence features better, this paper presents a method of feature extraction algorithm based on Fisher Discriminance. First, extract the profile silhouette by background cut off method, and then get the gait cycle by the change of their center of mass, at last convert the feature space. Then weight the gait characteristic matrix by Fisher Discriminance to make characteristics of the gait more obvious. Finally, test the algorithm by Nearest Neighbor Method.The results show that this algorithm has a good performance of recognition.

http://ieeexplore.ieee.org/iel7/6483173/6491698/06491800.pdf

Body gait tracking with a Fisher Discriminance on feature analyse

In this paper, we discuss hand modeling, constraint analysis and associated tracking. We take advantage of several correlations between joint rotations in the hand to reduce the number of degrees of freedom in the model and provide a simple and intuitive model. In the second step, an improved tracking algorithm is proposed to detect the region of the fingers. The temporal evolution of the probability density is achieved through sampling from the prior distribution, followed by local optimization of the sample positions to obtain updated modes. It is a good precondition for gesture recognition.

http://ieeexplore.ieee.org/iel5/8405/26504/01179947.pdf

A method of hand tracking based on articulated model

In this paper we deal with the tracking problem of human hand. The algorithm consists of two steps: At first the move part is detected and recognized with a advanced DOF model; then the curve tracking analysis is applied to get the key features with more details. The camera based on computer vision has the potential to provide much more natural, non-contact tracking. Although move part such as arms has a more faster speed, it can also been located exactly. With the semantic breakpoints extracted, some active part in a complex background can be location at any time in a test period.

A novel uncalibrated human hand tracking based on transition model

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Papers

A Real-time Gesture Recognition Algorithm on Video Surveillance

Body gait tracking with a Fisher Discriminance on feature analyse

A method of hand tracking based on articulated model

A novel uncalibrated human hand tracking based on transition model