05-11-2012, 05:44 PM
Survey on Various Gesture Recognition Technologies and Techniques
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ABSTRACT
Gestures considered as the most natural expressive way for communications between human and computers in virtual system. Hand gesture is a method of non-verbal communication for human beings for its freer expressions much more other than body parts. Hand gesture recognition has greater importance in designing an efficient human computer interaction system. Using gestures as a natural interface benefits as a motivation for analyzing, modeling, simulation, and recognition of gestures. In this paper a survey on various recent gesture recognition approaches is provided with particular emphasis on hand gestures. A review of static hand posture methods are explained with different tools and algorithms applied on gesture recognition system, including connectionist models, hidden Markov model, and fuzzy clustering. Challenges and future research directions are also highlighted.
INTRODUCTION
Gestures and face expressions easily used for daily humans interactions [1] while human computer interactions still require understanding and analyzing signals to interpret the desired command that made the interaction sophisticated and unnatural [1]. Recently the designing of special input devices witnessed great attention in this field to facilitate the interaction between humans and computers [2], and to accomplish more sophisticated interaction through the computer [2]. It is worth to mention that the window manager is the earlier user interface to communicate with computers [3]. The combining of traditional devices mouse and keyboard with the new designed interaction devices such as gesture and face recognition, haptic sensors, and tracking devices provides flexibility in tele-operating [2], text editing [4], robot control [2], cars system control [2], gesture recognition [4], Virtual Reality (VR) [5], and multi-media interfaces [4], video games [4].Gesture considered as a natural way of communication among people especially hear-impaired [6].
HAND GESTURE TECHNOLOGY
For any system the first step is to collect the data necessary to accomplish a specific task. For hand posture and gesture recognition system different technologies are used for acquiring input data. Present technologies for recognizing gestures can be divided into vision based, instrumented (data) glove, and colored marker approaches. Figure 1 shows an example of these technologies.
3D Model Based Approaches
Model based approaches used 3D model description for
modeling and analysis the hand shape [17]. In these
approaches search for the kinematic parameters are required
by making 2D projection from 3D model of the hand to
correspond edges images of the hand [7], but a lot of hand
features might be lost in 2D projection [7]. 3D Model can be
classified into volumetric and skeletal models [16][24].
Volumetric models deal with 3D visual appearance of human
hand [16] and usually used in real time applications [5][24].
The main problem with this modeling technique is that it deals
with all the parameters of the hand which are huge
dimensionality [16]. Skeletal models overcome volumetric
hand parameters problem by limiting the set of parameters to
model the hand shape from 3D structure [16][5]. Figure 2
shows 3D model approaches.
GESTURE RECOGNITION TECHNIQUES
The recognition of gesture involves several concepts such as pattern recognition [19], motion detection and analysis [19], and machine learning [19]. Different tools and techniques are utilized in gesture recognition systems, such as computer vision [6], image processing [6], pattern recognition [6], statistical modeling [6].
Artificial Neural Networks (ANN)
The use of neural networks for gesture recognition has been examined by many researchers. Most of the researches use ANN as a classifier in gesture recognition process, while some others use it to extract the shape of the hand, as in [25]. Tin H. [26] presents a system for hand tracking and gesture recognition using NNs to recognize Myanmar Alphabet Language (MAL). Adobe Photoshop filter is applied to find the edges of the input image and histogram of local orientation employed to extract image feature vector which would be the input to the supervised neural networks system. Manar M. [27] used two recurrent neural network architectures to recognize Arabic Sign Language (ArSL). Elman (partially) recurrent neural networks and fully recurrent neural networks have been used separately. A colored glove used for input image data, and for segmentation process, HSI color model is applied. The segmentation divides the image into six color layers, one for the wrist and five for fingertips. 30 features are extracted and grouped to represent a single image, fifteen elements used to represent the angles between the fingertips and between them and the wrist [27], and fifteen elements to represent distances between fingertips; and between fingertips and the wrist [27]. This input feature vector is the input to both neural networks systems. 900 colored images were used as training set, and 300 colored images for system testing. Results had shown that fully recurrent neural network system (with recognition rate 95.11%) better than the Elman neural network (with 89.67% recognition rate).
Histogram Based Feature
Many researches have been applied based the histogram, where the orientation histogram is used as a feature vector [32]. The first implementation of the orientation histogram in gesture recognition system and real time was done by William F. and Michal R. [32]; they presented a method for recognizing gestures based on pattern recognition using orientation histogram. For digitized input image, black and white input video was used, some transformations were made on the image to compute the histogram of local orientation of each image, then a filter applied to blur the histogram, and plot it in polar coordinates. The system consists of two phases; training phase, and running phase. In the training phase, for different input gestures the training set is stored with their histograms. In running phase an input image is presented to the computer and the feature vector for the new image is formed, Then comparison performed between the feature vector of the input image with the feature vector (oriented histogram) of all images of the training phase, using Euclidean distance metric and the less error between the two compared histograms will be selected. The total process time was 100 msec per frame.
Fuzzy Clustering Algorithm
Clustering algorithms is a general term comprises all methods that partitioning the given set of sample data into subsets or clusters [35] based on some measures between grouped elements [12]. According to this measure the pattern that share the same characteristics are grouped together to form a cluster [12]. Clustering Algorithms have been widely spread because of their ability of grouping complicated data collections into regularly clusters [35]. In fuzzy clustering, the partitioning of sample data into groups in a fuzzy way are the main difference between fuzzy clustering and other clustering algorithm [12], where the single data pattern might belong to different data groups [12].
CONCLUSION & FUTUREWROK
Building an efficient human-machine interaction is an important goal of gesture recognition system. Many applications of gesture recognition system ranging from virtual reality to sign language recognition and robot control. In this paper a survey on tools and techniques of gesture recognition system have been provided with emphasis on hand gesture expressions. The major tools surveyed include HMMs, ANN, and fuzzy clustering have been reviewed and analyzed. Most researchers are using colored images for achieving better results. Comparison between various gesture recognition systems have been presented with explaining the important parameters needed for any recognition system which include: the segmentation process, features extraction, and the classification algorithm.