Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.

Abstract. Human Action Recognition is one of the intriguing research area of modern Artificial Intelligence and Computer Vision where different techniques are followed to distinguish various human actions. Accuracy of such methods mainly depend on how a sequence of action frames can be represented by a number of most distinguishable frames, otherwise called key frames. In this paper, we have introduced an efficient method to extract key frames by maximizing accumulation of motion between frames for recognizing human actions using the help of 3D skeletal joint locations. Our feature representation is the combination of histogram of joint 3D (HOJ3D) and static posture feature of 3D skeletal joint locations. Then we used Hidden Markov Model (HMM) for human action recognition from the extracted frame sequence.