Static gesture language identification method based on KNN algorithm and pixel ratio gradient features

Abstract

The invention discloses a static gesture language identification method based on a KNN algorithm and pixel ratio gradient features. The method comprises following steps: 1. shooting color images; 2. performing binaryzation on the images according to color features of the images; 3. positioning hands according to shape features of the images and segmenting the image; 4. normalizing the segmented images; 5. extracting pixel ratio gradient features of the images and using the extracting pixel ratio gradient features as feature vectors of the images; 6. calculating the Euclidean distance between the input feature vectors and the a standard gesture language image feature library; 7. determining optimal matching results based on the KNN algorithm; and 8. outputting the identification results. According to the invention, feature matching is carried out with the combination of the color features, shape features and pixel ratio gradient features of the images and by the use of the KNN algorithm, so that the identification rate is improved, the adaptability for different environments is enhanced, the algorithm is relatively simple, the complexity is low, the system operation speed is rapid, and the equipment is low in cost.

Description

technical field [0001] The invention belongs to the technical field of static sign language recognition, in particular to a static sign language recognition method for image segmentation, positioning, feature extraction and pattern recognition. Background technique [0002] It is understood that there are more than 20 million people with hearing and language disabilities in my country, and the number is increasing at a rate of 20,000 to 30,000 every year. Research on sign language—the most important means of communication for this group of people—will not only help to improve and improve the living, learning and working conditions of these disabled people, and provide them with better services, but also can be applied to computer-assisted Mute language teaching, bilingual broadcasting of TV programs, virtual human research, special effects processing in film production, animation production, medical research, game entertainment and many other aspects. [0003] The developme...

AI Technical Summary

Problems solved by technology

This method can make human-computer interaction natural and convenient, and at the same time, only some cheap equipment such as digital camera equipment is used, and the economic investment is low; Not accurate enough, resulting in no guarantee of robustness in subsequent feature extraction and detection stages

Therefore, it is less applied to the recognition of a large number of words

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