The Research Of Automatic Fingerprint Identification System Algorithm

Automatic tracking of moving target is high technological subject which employs advanced achievements in many fields such as image processing, pattern recognition, artificial intelligence and automatic control, etc. It has wide application in many fields such as intelligent automatic weapn, intelligent robots, autonomous vehicle guidance, biology and so on.In this paper, three tracking methods are researched with the help of many experiments: (1) difference, (2) correlation, (3) active contour.First, difference is studied. Its main idea is that two images are subtracted, and some information of moving target is derived from the difference image. Obviously, this method is very simple and convenient to operate. However, it also has its drawbacks. The premise of application of this method is that background should be quite stable and has no great changes. But in practical images, the illumination changes and the noise from around environment are unavoidable, which result in low reliability when detecting and tracking moving target.Second, correlation-based method, also called template-matching method, is explored. For one thing, ordinary matching without any improved methods is researched, and it is found that this method cannot be applied to tracking moving target at all due to huge time consumption. Therefore, multi-resolution matching is researched. Its main idea is that coarse matching is operated in the lower resolution image, obtaining some waiting regions, then fine matching is applied to the waiting images with higher resolution, and find the position of moving target. JMR (Jump Multi-Resolution) and SMR (Smooth Muhi-Resolution) are studied respectively. Because the templates of above matching methods are both changeless, they have poor self-adaptability to the changes of moving target. Accordingly, an improved method integrated with Kalman Filter with better performance is introduced, which has two key techniques-prediction and update. Prediction's main idea is to take full advantage of correlation between frames, and to predict the possible positions of moving target based on previous data. The matching is justcompleted in the possible region, which improve its speed due to reduction of waiting region. Update's main idea is that the matched region in current frame is regarded as the template of moving target for next frame and repeats like this, which makes the template updated constantly and has better adaptability to the change of target.In recent ten years, active contour (also called "snake") has begun to be applied to moving target tracking. Investigation shows that "snake" is suitable for rigid or nonrigid target tracking. Starting with researching on defining and computing of snake, three computing method are analyzed, and Greedy Algorithm is chosen as the final method. Based on above, the operation mechanism of snake is explored deeply from different view such as resampling of snake pixel (snaxel), Balloon model, each energy's influence on snake, noise's influence on snake and so oa In order to avoid the drawback of invarible snaxel in original model, snaxel resampling is introduced, which can increase or reduce the number of snaxel adaptively and render the resolution of snake keep invarible during its evolution. Followed that, Ballon model and its improved model are studied successively. After determination of each energy item, their weights' influences on snake are researched through a great deal of experiments. In pratical image-captureing system, noise is inevitable; therefore, salt and pepper noises' influences on snake are researched respectively, and several methods of noise reducing like median value filtering and self-adaptive smooth filtering are introduced. Finally, two applications of snake-tracking of moving target and location of iris-are presented.