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Hand Gesture Detection and Recognition System


Recent developments in computer software and related hardware technology have provided a value added service to the users. In everyday life, physical gestures are a powerful means of communication. They can economically convey a rich set of facts and feelings. For example, waving one’s hand from side to side can mean anything from a “happy goodbye” to “caution”. Use of the full potential of physical gesture is also something that most human computer dialogues lack.

The task of hand gesture recognition is one of the important and elemental problems in computer vision. With recent advances in information technology and media, automated human interactions systems are build which involve hand processing task like hand detection, hand recognition and hand tracking.

This prompted my interest so therefore I planned to make a software system that could recognize human gestures through computer vision, which is a sub field of artificial intelligence. The purpose of my software through computer vision was to program a computer to “understand” a scene or features in an image.


Hand gesture detection and recognition system is first step to detect and localize hand in an image processing. The hand detection task was however challenging because of variability in the pose, orientation, location and scale. Also different lighting conditions add further variability.


The vast majority of hand gesture recognition work has used mechanical sensing, most often for direct manipulation of a virtual environment and occasionally for symbolic communication. Sensing the hand posture mechanically has a range of problems, however, including reliability, accuracy and electromagnetic noise. Visual sensing has the potential to make gesture interaction more practical, but potentially embodies some of the most difficult problems in machine vision. The hand is a non-rigid object and even worse self-occlusion is very usual.

Full ASL recognition systems (words, phrases) incorporate data gloves. Takashi and Kishinodiscuss a Data glove-based system that could recognize 34 of the 46 Japanese gestures (user dependent) using a joint angle and hand orientation coding technique. From their paper, it seems the test user made each of the 46 gestures 10 times to provide data for principle component and cluster analysis. The user created a separate test from five iterations of the alphabet, with each gesture well separated in time. While these systems are technically interesting, they suffer from a lack of training.

Excellent work has been done in support of machine sign language recognition by Sperling and Parish, who have done careful studies on the bandwidth necessary for a sign conversation using spatially and temporally sub-sampled images. Point light experiments (where “lights” are attached to significant locations on the body and just these points are used for recognition), have been carried out by PoiznerMost systems to date study isolate/static gestures. In most of the cases those are finger spelling signs (Klimis Symeonidis, August 23, 2000)


First objective of this thesis is to create a complete system to detect, recognize and interpret the hand gestures through computer vision

Second objective of the thesis is therefore to provide a new low-cost, high speed and colour image acquisition system.


Biometric systems are systems that recognize or verify human beings. Some of the most important biometric features are based physical features like hand, finger, face and eye. For instance finger print recognition utilizes of ridges and furrows on skin surface of the palm and fingertips. Hand gesture detection is related to the location of the presence of a hand in still image or in sequence of images i.e. moving images. Other biometric features are determined by human behavior like voice, signature and walk. The way humans generate sound for mouth, nasal cavities and lips is used for voice recognition. Signature recognition looks at the pattern, speed of the pen when writing ones signature.


Hand detection and recognition have been significant subjects in the field of computer vision and image processing during the past 30 years. There have been considerable achievements in these fields and numerous approaches have been proposed. However, the typical procedure of a fully automated hand gesture recognition system can be illustrated in the figure below:


There have been numerous researches in this field and several methodologies were proposed like Principle Component Analysis (PCA) method, gradient method, subtraction method etc. I have studied four different algorithms and I will choose one of them which will give me best results.


The scope of this project is to build a real time gesture classification system that can automatically detect gestures in natural lighting condition. In order to accomplish this objective, a real time gesture based system is developed to identify gestures.


Due to the time constraint and complexity of implementing system in C++, the aim was to design a prototype under


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