Gesture identification method and apparatus

Abstract

The invention discloses a gesture identification method. The method comprises: a shape of a to-be-identified gesture is obtained, a closed profile is extracted from a gesture-shaped edge, and all profile points on the profile and a coordinate of each profile point are obtained; the layer number of the profile is determined, and an area parameter, an arc length parameter, and a gravity center parameter that correspond to each profile point at each layer are calculated based on the coordinate of each profile point, wherein the parameters are used as feature parameters of the profile point; with the feature parameters of each profile point, a to-be-identified gesture and a template in a preset template base are matched to obtain an optimal matching template, and the optimal matching template is determined as the to-be-identified gesture. According to the invention, a global feature, a local feature, and a relation between the global feature and the local feature are described; multi-scale and omnibearing analysis expression is carried out; effective extraction and expression of the global feature and the local feature of the to-be-identified gesture shape can be realized; and a phenomenon of low identification accuracy caused by a single feature can be avoided.

Description

technical field [0001] The invention relates to a gesture recognition method and device, belonging to the technical field of target recognition. Background technique [0002] Gesture recognition is of great significance in the field of human-computer interaction, and has a wide range of applications in virtual reality, sign language recognition, and human-computer games. [0003] The difficulty of the previous gesture recognition technology lies in the acquisition of gestures. With the development of the depth camera, the Kinect sensor has solved this problem very well, and then the difficulty of gesture recognition has focused on the recognition efficiency and accuracy. [0004] Generally, gesture recognition methods can be divided into two categories. The first category is based on statistical methods: for a dynamic gesture, it is regarded as the output of a random process, and gesture recognition can be determined according to statistical models, such as PCA, HMMS, parti...

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Problems solved by technology

The first category is based on statistical methods: for a dynamic gesture, it is regarded as the output of a random process, and gesture recognition can be determined according to statistical models, such as PCA, HMMS, particle filter, enrichment algorithm, etc., but calculation Complex, low recognition efficiency, unable to meet real-time requirements

The second type is a rule-based method: first, a series of templates are preset according to the input features. When a gesture is recognized, a series of features of the gesture are obtained and matched with the preset template features, which match the input gesture best. The template is output as the category of the gesture to be recognized, such as Shape contexts, Thresholding+FEMD, Near-convex+FEMD, etc., but the recognition accuracy and recognition efficiency cannot be guaranteed at the same time

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