# Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps
## 2 Related Work  
In the gesture recognition, a hand segmentation is carried out first, which detects the hand position or hand shape. A popular segmentation method in the vision based system is skin color detection that extracts hand portion from cluttered background [5; 6]. [PERSON] et al. [7] proposed a multi-feature fusion method that improved recognition results by extracting angle count, skin color angle, and non-skin color angle in combination with Hu invariant moments features. Some gesture recognition systems simplified hand extraction from the background with the help of inexpensive color-coded gloves for hand segmentation. A glove providing color-coding with six unique colors were used in [8; 9]. [PERSON] and [PERSON] [10] employed an ordinary cloth glove being printed with a custom pattern that was designed to estimate the poses. Our previous work [3] also employed a two-colored glove for hand segmentation. Another option for gesture segmentation is the use of the 3D image that is taken through depth sensors, such as the Microsoft Kinect depth camera and the Leap Motion. The 3D camera views the subject in the front plane and generates a depth image of the subject, and the depth image is used for background removal, followed by the generation of the depth profile of the subject. Gesture recognition systems with the Kinect are found in [11; 12; 13; 14; 15]. [PERSON] et al. [16] used another depth camera called Time-Of-Flight range camera that supplied real-time depth information per pixel. In terms of applicability, the vision-based gesture segmentation is desirable since it requires only a conventional camera available on most laptop PCs, and no special depth sensor is needed. Unlike the Kinect sensor and other depth sensors, the output of the Leap Motion is the depth data which consists of palm direction, fingertips positions, palm center position, and other relevant points. Therefore, no extra computational work is needed to get these information. Due to its unique features, the Leap Motion has been applied to dynamic hand gesture recognition by by researchers. [PERSON] et al. [17] proposed a dynamic gesture recognition system, in which the Leap Motion was used to compute feature vector of the gesture, and a hidden conditional neural field (HCNF) classifier was used to recognize dynamic hand gesture. Another example is the work done by [PERSON] et al. [18]. Their hand gesture recognition system was based on the Leap Motion and a spatial fuzzy matching (SFM). Hand-eye coordination means the ability to combine seeing and hand movement. [PERSON] et al. [19; 20] examined the correlations between eye motion and the motion of the mouse cursor regarding hand-eye coordination, and they used an hand-eye tracking system which was made of the Leap Motion and Eye Tribe tracker. Challenge of real-time dynamic gesture recognition is the gesture spotting or temporal segmentation that detects when the gesture starts and ends. In the system proposed by [PERSON] et al. [13], each dynamic gesture was defined as a sequence of trigger-poses, and the start and end of the gesture were detected by finding the start and end triggers. [PERSON] et al. [21] used hand positions to perform the temporal segmentation by assuming that a user put hands-up pose at the start of gesture and put hands-down pose at end of the gesture. A real-time dynamic hand gesture recognition system proposed by [PERSON] et al. [15] used two hand configurations (open-hand, closed-hand) to achieve gesture spotting and its 3D motion trajectory of the dynamic gesture was captured by the Kinect sensor. These approaches disturb the natural flow of gesture, and thus a new approach that can detect the start and end of gesture naturally, is desirable. A static hand gesture recognition can be achieved by applying standard pattern recognition techniques such as template matching, whereas dynamic gesture recognition requires time-series pattern recognition algorithm such as a hidden Markov model (HMM) or dynamic time warping (DTW) algorithm.