A new method for fingerprint identification is proposed, using an ANFIS-based matching algorithm, which is suitable for large-scale identification systems. In this algorithm, the Gabor transform is used to extract features of fingerprints, while ANFIS is trained to identify the resultant Gabor features. Experimental verifications show that this proposed matching algorithm has high accuracy.
Fingerprint recognition involves in two main approaches: minutiae-based algorithm, the most popular and traditional, has several drawbacks and not suitable for the applications that using solid-state sensor; while structure-based algorithm, using the Gabor filters, captures rich discriminatory information contained in the gray level fingerprint image and generates a feature code with the same length, which is benefit for quickly matching because of the bit wise comparison. This paper probes into the optimal design of Gabor filters theoretically. The inference of optimal parameters of Gabor filters and the whole identification procedure is described in detail. Many comparison experiments are also considered carefully, such as the size of tessellation cells, etc. The experiment result shows the efficiency of the Gabor filters. And to reduce the identification time, we remain the main filters while maintaining same recognition accuracy.
Structure-based algorithm, using the Gabor filters, captures rich discriminatory texture information contained in the gray level fingerprint image. It can implement matching for the limited area image of fingerprint, when the performance of traditional minutiae-based algorithm not very well because of lack of sufficient feature points in the overlap area. This method not only suits for the small area image from widely used solid-state sensor, but also acts as the complement of minutiae-based matching. Moreover, it generates a feature code with the same length, which is benefit for quickly matching. Two methods involved are described in this paper: one is the optimal design of Gabor filters, theory and methodology; the other is whole matching strategy and procedure. The core point and its director are detected to achieve the translation and rotation invariant. Many comparison experiments are also considered carefully, such as the size of tessellation cells, etc. The experiment result shows the efficiency ofthe Gabor filters and matching method. The analysis and future work is given in the end.
License plate automatic recognition is an important section of traffic management. In this paper, the procedure of license plate recognition is analyzed. A novel compact hybrid opto- electrical correlator and the optimum design of spatial matched filter (MSFs) are illustrated in details. The compact correlator, combining parallelism of optics and flexibility of electronics, plays a promising part in the field of image recognition. The optimal binary phase-only MSFs, based on Synthetic Discriminate Function (SDF), by means of NN Clipping and Monte Carlo learning algorithm, can greatly improve the accuracy of image recognition. The result is presented finally.