Depending mainly upon whether a keyboard or a mouse is used as an interface between a computer and a user, an algorithm for recognizing a user, that is, a method for extracting user's face or hands has been considered important.
Generally, a method for detecting user's hands to be recognized from an image photographed through a camera uses unique characteristic information such as color, shape, size, and so forth of a hand and has limits to detection because only one hand can be mainly detected and tracked. Upon detection of both hands, it is difficult to distinguish between a right hand and a left hand because characteristic information such as their color, sizes and shapes is similar; and it is not easy to make a judgment when hands overlap objects having characteristic information similar to the hands. Namely, since distinct characteristic information that can distinguish between both hands is not sufficient, there are difficulties in simultaneous detection of both hands.
Conventional methods for distinguishing between both hands includes a method for identifying all types where both hands are overlapped and a method for calculating a distance between a hand and an elbow by applying a distance conversion function to shading information from the hand to the elbow. Such hand detection methods should extract the locations of user's hands and face using skin color information; however, it is very difficult to accurately extract the locations of hands and face through the recognition of skin color using only color images.
Meanwhile, a gesture including a hand motion is one of human various communication methods and many researches on interaction with a computing machine using the gesture are being in progress. If algorithms for detecting hands are robustly achieved, they will be able to be effectively used in various multimedia based industries. Among them, a hand detection method can be usefully employed for hand gesture recognition and is a very useful technique in developing an interface between a computer and a user to be more convenient for the user.
In the above-described method for identifying types where both hands are overlapped, it is difficult to detect both hands if another overlap except for the identified types occurs. Even though the method is compensated for, there are many types of operations required for the location detection of both hands. These operations for location detection of both hands are complicated. Even in the method using the distance conversion function, a whole image shown from hands to heels is indispensable. In this case, since the number of gestures that a user can make is small, it is not suitable to actually use the method using the distance conversion function. Even in the method for detecting the locations of both hands by extracting characteristic information of both hands, an operation time is abruptly increased when increasing the amount of characteristic information in priority to improve tracking performance. Especially, upon detection of hands using skin color, erroneous detection of a serious level may occur due to the presence of an object having color similar to skin color or to an environmental factor such as the reflection of lighting.
Meanwhile, in hand shape detection, if hand shapes can be effectively extracted even while a user freely moves, various applications in real life will be possible.
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