Abstract
This paper studies vision based gesture recognition related fields on Android
system, including gesture detection, gesture segmentation and gesture recognition,
designs and implements a static gesture recognition system on Android, this system can
recognize 6 specific static gestures in real-time. It offers new interactive means for
Android applications.
First，for gesture detection, this paper presents the Viola-Jones gesture detection
method based on LBP feature, which has been widely used in face recognition, but is
found much less applications to gesture recognition. Compared to the Viola-Jones
method based on Haar-like feature, this method has equivalent hit rate, but much less
computations. So it is a better choice for embedded devices.
Secondly, this paper introduces the hand segmentation scheme which consists of
three processes: skin color detection, edge detection and contour extraction. Then the
gesture recognition algorithm is presented, the 6 cluster centers of the 6 hand gesture is
calculated by using PCA and K-means clustering, the recognition result is determined
by the distance between a hand gesture feature vector and the 6 cluster centers.
Finally, with the help of Android NDK compilation tools, and OpenCV for Android
vision algorithm library，this paper implements the algorithm described above on
Huawei S7 Slim Tablet, which is equipped with a single-core 1GHz processor, and
Android 2.2 operating system. Besides, this paper designs an Android application
programming interface for six static gesture detection and recognition. As an application
case of this interface, an Android application is also shown in this paper, which takes the
hand gesture as input commands to take photos. In this case, the average processing
time of detection and recognition for a 352 * 288 pixel image frame is 91ms.
Keywords: Android Gesture Detection Hand Segmentation Gesture
Recognition K-means Clustering
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