Research On Multi-Sample, Multi-Unit Multi-View, Multi-Modal Biometric Recognition

Influenced by data noise and limitation of recognition system itself, the accuracy of identification system based on single biometric trait proves to be quite limited. Therefore, the reaserch of using multi-biometric recognition for improving recognition accuracy has become one of the hotspots in biometric recognition field. Combining with specific biometric recognition, especially fingerprint recognition, this paper studies multi-biometric recognition based on matching score from the aspects of multi-sample feature template selection, simulated data and real data based multi-unit recognition, multi-view touchless fingerprint recognition, multi-modal recognition and etc., aiming at providing possible solution to problems in multi-biometric recognition process. The main work and contributions are as follows:1. For multi-sample template selection, this paper proposes two algorithms, MMS (Maximum Match Scores) and GMMS (Greedy Maximum Match Scores). MMS and GMMS algorithms do not involve details of primitive biometric feature data and eliminate the necessity of thorough feature extrcation process. Therefore, the two algorithms are more flexible and can be used in various biometric systems. They prove to be effective for template selection compared with the existing algorithms. For template updating, we put forward two methods, online and offline methods. Each of the two strategies has its own advantages and disadvantages. The experimental results show that offline strategy can get better performance.2. For multi-unit recognition system, this paper proposes a fusion algorithm Square. For multi-unit recognition system, this paper first compares identification accuracy of different fusion rules based on pseudo multi-unit data, and then put forward a new fusion rule:Square. According to our simulated data experimental results, Square and Sum rule could respectively achieve the best recognition performance when FAR is smaller and bigger. We then design, based on the advantages of Square and Sum rule, a new Square-Sum rule, which could always get better recognition performance.3. Based on real multi-unit fingerprint data, this paper studies the recognition performance of single finger, performance of multi-finger fusion, correlation between different fingers, and proposes an improved Sum rule for multi-unit biometric systems. Through a number of experiments based on MCYT-Fingerprint dataset and with the combination of previous studies, this paper compares the recognition performance of single fingerprint, analyzes recognition performance of multi-finger fusion, studies the relativity of different fingers, and proposes an improved Sum rule for multi-unit biometric systems as well as verifying that the improved Sum can gain better performance.4. For multi-view touchless fingerprint recognition, this paper proposes a Cluster-based Dynamic Score Selection algorithm CDSS. We collect a small-scale multi-view touchless fingerprint database, study the image preprocess, feature extraction and feature matching of touchless fingerprint, and basically implement touchless fingerprint recognition. This thesis studies multi-view touchless fingerprint recognition based on matching score fusion, then puts forward CDSS multi-view touchless fingerprint recognition fusion method. After obtaining the new features of different matching score through clustering, we flexibly select different fusion rules as the final fusion strategy on the basis of these new features and statistics value size comparison of matching score. Compared with single-view fingerprint recognition, multi-view touchless recognition could greatly improve the recognition performance of the system. At the same time, CDSS is compared with Sum, Max, SVM and Fisher linear discriminate, which proves the method of this paper gets better recognition performance.5. For multi-model recognition, this paper proposes fusion algorithms based on FAR and FRR. This paper firstly proposes a new confidence-based fusion strategy based on FAR and FRR. As confidence-based strategy is established on the basis of FRR and FAR, it could both avoid directly accessing the posterior probability of certain scores, and dipict the distribution of scores. Then we improve the confidence-based algorithm and propose a fusion algorithm using SVM based on FRR and FAR. This algorithm could utilize both some overall situation information, that is the corresponding FAR and FRR value of matching score, and the good classification capacity of SVM. During implementation, this paper first calculates the transformation value of score having appeared in training set, and taking these values as fixed nodes, calculates the transformation value of matching scores having appeared in testing set by method of interpolation. When looking for the left and right node for interpolating value, this paper adopts binary searching strategy which greatly reduces computation. The experimental results show that the algorithms could achieve better recognition performance.