# Movement-in-a-Video Detection Scheme for Sign Language Gesture Recognition Using Neural Network
## 3 Result and Discussion  
In this study, videos of Argentinian and Chinese sign language gestures from LSA64, DEVISIGN-D, and SLR_Dataset were used. First, the frames of each video were extracted. The number of frames depends on the duration of the gestures. The results of frame extraction from the videos in the three datasets are shown in Figure 7.  
As shown in the figure, LSA64 contains videos of sign language gestures with an average of three hand and arm poses. Therefore, these are classified as short gestures. DEVISIGN-D contains videos of sign language gestures with an average of five hand and arm poses. Therefore, these were classified as medium gestures. SLR_Dataset contains videos of sign language gestures with more than five hand and arm poses. Therefore, these are classified as long gestures. Figure 6: Sum and difference frame result of examples illustrated in **(a)** Figure 4 and **(b)** Figure 5.  
Figure 7: Examples of frame sequences extracted from a video in **(a)** LSA64, **(b)** DEVISIGN-D, and **(c)** SLR_Dataset. For the purpose of analysis, further examples of the result of extracting the frame sequence of short gesture videos (LSA64) are shown on Figure 8. In the figure, both the motion and pose of the hands while performing the sign language gesture can be inferred. Thus, the sign language gesture can be visualized regardless of the absence of frames in between those shown. Figure 8a shows a few of the frames extracted from a video recorded outdoors (natural lighting). The sign language gesture for the word \"opaque\" involves two hand poses at two positions. This gesture can be performed by first copying the hand's position and pose in the first frame and then copying the hand's position and pose in the second frame. Regardless of the speed of transition between two hand poses and positions, the combination of the two constitutes the gesture for \"opaque\". Unlike the sign language for \"opaque\", those for \"red\" and \"green\" involve one hand pose each. However, to perform the gesture, the hand is situated in different positions. The gesture begins with the hand positioned below the face. Then, it is raised above the face and finally dropped below the face again, returning it to its initial position. Figure 8b shows a few of the frames extracted from a video recorded indoors (artificial lighting). It is evident that, similar to Figure 8a, the gesture can be performed by copying the pose and position of the hand in each frame consecutively. An exception to this is the sign language gesture for \"  
Figure 8: Six examples (out of 64) of frame sequences extracted from videos of sign language gesture recorded (**a**) outdoors and (**b**) indoors. from the hand pose and position, the gesture involves movement of the head as well. An examination of the frames enables visualization of the sign language gesture when the frames are in order. Thereby, the need for viewing the video is circumvented. To demonstrate the effect of the proposed method further, we classify the gestures in the LSA64 dataset into three based on similarity: (1) identical, (2) similar, and (3) unique gestures. This is apart from the classification according to lighting. An example of each gesture classification is shown in Figure 9a,b. These figures reveal the following: (1) The gestures for \"red\" and \"green\" exhibit highly identical paths of hand and differ only in the hand pose. (2) The gestures for \"away\" and \"colors\" exhibit a similar path of hand. However, notwithstanding the similarities in the path of hand (rightward away from the gesture), there is an observable difference between these. (3) The paths of hand for unique gestures are different. These observations are expected to be reflected in the result of the sum and frame difference method. This is explained next. After extracting the frame sequence, each frame was subtracted from the previous frame. Thereafter, the differences were added.