Study On The Novel Technique Of Gait Recognition Based On Wavelet Transform And Support Vector Machine

Recognition by gait is a new field for the biometric recognition technology. Its aim is to recognize people or detect physiological, pathological and mental characters by their walk style. The future of this technique will be very good. Gait recognition, as one of the attractive research area of biomedical information detection, attracts more and more attention. Gait recognition mainly analyzes moment images including walk style. For this technique the key factors is to find out the gait characters and classification method. It contains many kinds of techniques, such as computer vision, pattern recognition, video and image sequences processing and etc.This study deals with the following process. Body silhouette sequences of gait were extracted and normalized in this study. The sequences were added together and gait character image could be obtained. The method of wavelet transform was used to decompose the image of gait character. In this paper two models were combined for the body object. At the same time moment invariants and skelecton theory were used in extracting gait character parameters. Then support vector machine (SVM) was applied for classification and recognition. This technique was applied to CASIA and USF gait data-set and achieves probability of correct recognition of 93.67% for the former and 83%-100% for the latter.Considering the different environment, the infrared thermal imaging technology was used to research gait recognition. Infrared imaging camera was presented for the TJU Infrared Gait Database (TIGD). The experiment was performed in this data-set. It is proved that recognition result was insensitive for the person with object such as backpack and ball. For the term of wearing down coat, recognition rate is affected apparently. So we should give attention to the study and application for infrared gait recognition. In this paper, the application of gait recognition was engaged in research. The experiment platform of automatic gait access control system was established. This system can be used to watch body objects. The function involves of real-time image collection, image orientation, feature extraction, recognition and etc.The originalities of this thesis were the followings:i. For the first time, two models of body were united in the filed of gait recognition. This method can repair the deficiency of using single model. Feature parameter of skelecton and moment invariants was applied together in gait recognition. This technique reduces the influence by noises such as background, light, clothes, speed and etc. At the same time it strengthens the application for practice.ii. For the first time, wavelet transform and support vector machine were combined to use recognize gait. It enforces the precision of classification. Moment invariants and wavelet transform were used to extract feature parameter of gait. This method can function as an efficient gait recognition based on body silhouette and area feature.iii. The new concept of using infrared imaging camera in gait recognition developed the attractive research areas. Infrared imaging camera can be used in night and transforms the temperature of body to the image. The level of research was improved in image collection of gait sequences and gait recognition.iv. This new access control system based on gait feature was built. The software of this system was compiled. Elementary work was researched in the application involving gait recognition.