Hand gestures have recently been regarded as an important communication modality in human-computer interaction. These gestures are considerably advantageous over traditional interfaces such as the keyboard and mouse given their convenience and naturalness. Hand gesture interfaces have been successfully employed in many areas over the past 20 years

including robotics

sign language communication

clinical medicine

and the entertainment industry. Recognition is the key technique in most interaction systems based on hand gestures. Such recognition can be grouped into two categories: dynamic and static hand gesture recognition. Most existing methods for static hand gesture recognition usually perform poorly given the large number of hand gesture categories. In this study

we propose a hierarchical method of static hand gesture recognition that combines finger detection and histogram of oriented gradient (HOG) features to overcome this limitation. A hierarchical strategy is designed based on the number of outstretched fingers to solve the “large-category” problem described above. The basic idea is as follows: Entire categories of gestures are divided into several subsets according to the number of outstretched fingers. This number is first recognized by a certain finger detection method as an input gesture. Then

we derive the appropriate gesture from the subset that contains the gestures featuring the given number of fingers via a certain feature extraction approach. Thus

the recognition problem is solved in two stages. Following the first finger-based stage

the number of categories that must be addressed by the second layer decreases significantly. To achieve this target

we propose a new finger detection method based on morphological operations. The proposed method consists of the following steps. First

the hand region is detected with a skin color model. Then

the number of outstretched fingers is identified by the finger detection method. Accordingly

a support vector machine classifier is selected from a set of pre-classified classifiers. Finally

we calculate the HOG feature of the input gesture

which is then inputted into the selected classifier to obtain a result. In our experiments

25 popular static gestures are tested to verify the effectiveness of the proposed recognition method. The one-stage HOG-based recognition method is mainly used for comparison. Experimental results demonstrate that the proposed method can improve the recognition accuracy of the conventional HOG-based method by approximately 20%.This study makes three main contributions to literature. First

we present a new finger detection method that can accurately determine finger position and identify the number of outstretched fingers. Second

we design a finger-based hierarchical recognition strategy that can effectively overcome the “large-category” limitation for conventional one-stage recognition methods. Third

we propose a two-stage method of static hand gesture recognition that combines finger detection and HOG features. When hand regions can be detected effectively

the proposed method can perform satisfactorily with real-time efficiency. In the future

we will design a practical system for hand gesture recognition that employs a robust hand detection approach. Furthermore

the presented finger detection method can be used to normalize rotations as necessary.