In this study, the authors extend and refine the process of fingerprint retrieval, with the goal of boosting recognition rates for the first rank candidate and low penetration rates. On top of a baseline retrieval system which extracts Gabor features in multiple directions from fingerprint images, the authors propose spatial modelling techniques to generate artificial samples for training the system. Translational modelling, rotational modelling and distorted sample generation techniques are used to augment the original training set in order to boost the accuracy of fingerprint retrieval. The effectiveness of the models is evaluated using the well‐known National Institute of Standards and Technology database 4. Experimental results, with reference to some leading fingerprint retrieval rates reported in the literature, confirm that the authors’ proposed system is promising in recognition performance.

The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simplek-nearest neighbor (k-NN) classification. However, thek-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-Nfingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved.

The problem of offline handwritten Chinese character recognition has been extensively studied by many researchers and very high recognition rates have been reported. In this paper, we propose to further boost the recognition rate by incorporating a distortion model that artificially generates a huge number of virtual training samples from existing ones. We achieve a record high recognition rate of 99.46% on the ETL-9B database. Traditionally, when the dimension of the feature vector is high and the number of training samples is not sufficient, the remedies are to (i) regularize the class covariance matrices in the discriminant functions, (ii) employ Fisher's dimension reduction technique to reduce the feature dimension, and (iii) generate a huge number of virtual training samples from existing ones. The second contribution of this paper is the investigation of the relative effectiveness of these three methods for boosting the recognition rate.

Routing is a process of finding a network path from a source node to a destination node. A good routing protocol should find the "best path" from a source to a destination. When there are independent constraints to be considered, the "best path" is not well-defined. In our previous work, we developed a line segment representation for Quality-of-Service routing with bandwidth and delay requirements. In this paper, we propose how to adopt the line segment when a request has two concave constraints. We have developed a series of operations for constructing routing tables under the distance-vector protocol. We evaluate the performance through extensive simulations.