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.

One of the attractive methods for providing natural human-computer interaction is the use of the hand as an input device rather than the cumbersome devices such as keyboards and mice, which need the user to be located in a specific location to use these devices. Since human hand is an articulated object, it is an open issue to discuss. The most important thing in hand gesture recognition system is the input features, and the selection of good features representation. This paper presents a review study on the hand postures and gesture recognition methods, which is considered to be a challenging problem in the human-computer interaction context and promising as well. Many applications and techniques were discussed here with the explanation of system recognition framework and its main phases.

Segmentation is the classification of the input colored image into skin and non-skin pixels based on skin color information. A wide range of applications that require the segmentation process as a preprocessing operation such as computer vision, face/ hand detection and recognition, medical image analysis, and pattern recognition. Color information is one of the simple cues used for detecting skin color, and the use of proper color space to represent color information of an image is a crucial decision. In this literature different segmentation techniques are presented, examples and comparison between the main three based segmentation techniques are given as well. Skin color modeling based statistical model is explained in detail, with discussion the combination with different segmentation techniques. The selection of appropriate segmentation method depends on the application and system environments. The performance of any segmentation algorithm is quantified using some benchmarking such as recall and precision coefficients, or by calculating the percentage of correct and false detection rates according to the complexion of the technique used.

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In computer vision, object detection is a basic process for advanced procedure forms such as object detecting, analyzing, tracking, etc., for further processes, features extraction play a vital part to identify the objects accurately. Most of the existing frameworks may not be able to distinguish the objects appropriately when different objects have a place to a single frame. In this work, an automatic detection and recognition system of two-dimensional geometric shapes have been proposed. Firstly applied Genetic Algorithm (GA) to fill the shape after performing proper segmentation pre-processing method. The proposed framework is able of identifying numerous objects in the input image, determining the sort of the identified object, and labeled the recognized objects. Statistical method has been applied for each shape to extract the objects corners' points by calculating the largest boundary to form the features vector. Ultimately, the identified objects are classified as geometrical shapes such as square, rectangular, triangular, or circular. The proposed method achieved high accuracy around 98.3%, and an average computational time 0.521 sec. as an effective classification technique.