Chih-Jen Lee

Fingerprint recognition using principal Gabor basis function

Based on the Gabor transform, we propose a unified viewpoint for fingerprint image representation and recognition. In a local fingerprint image, we demonstrate that inter-ridge distances and ridge directions can be obtained by the principal Gabor basis function (GBF) directly. In addition, according to the locations of principal GBF on the spatial-frequency plane, the similarities of two local ridge structures can be measured easily. As compared with the global Gabor-based approach, the proposed method not only saves memory space, but also reaches a higher recognition rate.

A Gabor filter-based approach to fingerprint recognition

We propose a Gabor-filter-based method for fingerprint recognition in this paper. The method makes use of Gabor filtering technologies and need only to do the core point detection before the feature extraction process without any other pre-processing steps such as smoothing, binarization, thinning, and minutiae detection. The proposed Gabor-filter-based features play a central role in the processes of fingerprint recognition, including local ridge orientation, core point detection, and feature extraction. Experimental results show that the recognition rate of the k-nearest neighbor classifier using the proposed features is 97.2% for a small-scale fingerprint database, and thus that the proposed method is an efficient and reliable approach.

Fingerprint recognition using directional micropattern histograms and LVQ networks

The paper is about designing a fingerprint recognition system that makes use of the directional micropattern histograms of a fingerprint image for local ridge orientation calculation, core point detection, and feature extraction. An improved learning vector quantization network is also proposed to avoid the unfairness of the winning rate and to determine a proper number of hidden units. Experimental results show that the recognition rate of the proposed method is 99.62% for a small-scale fingerprint database.

Methods to enhance the performance of Iterated Timing Analysis algorithm

In circuit simulation, Relaxation-based algorithms have been proven to be faster and more flexible than the standard direct approach used in SPICE. ITA (Iterated Timing Analysis) algorithm has even been widely used in industry. This paper describes a new accelerating technique using the latency property of subcircuits and other accelerating methods based on Strength of Signal flow (SSF) for ITA algorithm. The definition of SSF, calculation method, and methods to utilize SSF all have been illustrated clearly. Experimental examples are given to justify the superior property of these proposed methods. A more powerful strategy for enhancing ITA is also designed and carefully justified.

Direct Minutiae Matching in Gray-Level Fingerprint Images

Most researchers applied the same procedure in both fingerprint enrollment and fingerprint verification. In general, many overall pixel-level computations are employed in the procedure of fingerprint enrollment to obtain clear minutiae. Because the minutiae-matching is performed only once for one-to-one verification, we propose a different procedure to directly check the positions of minutiae in gray-level fingerprint images. Therefore, this approach avoids some time-consuming overall pixel-level computations and achieves a fast minutiae matching

Waveform Relaxation-Based Large-Scale Circuit Simulation for MOSFET and Lossy Coupled Transmission Line Circuits

In this paper, we propose a modified Waveform Relaxation algorithm to perform large-scale circuit simulation for MOSFET circuits containing lossy coupled transmission lines that have been encountered in modern circuit design community, in which a full time-domain transmission line calculation algorithm based on the Method of Characteristic is adopted. New software techniques are proposed to enhance the robustness as well as efficiency of the simulation process. All proposed methods have been implemented and executed to justify the claimed advantages.

Apply principal Gabor basis functions to extract discontinuous points from low-resolution binarized fingerprint images

In fingerprint images, the discontinuous points include cores, deltas, minutiae, and etc. Generally, cores and deltas are applied to fingerprint classification and minutiae are to fingerprint matching. Owing to complicated processes, most researchers developed minutiae extraction algorithm from 500 dpi fingerprint images in the past. Therefore, much time is wasted before minutiae are extracted. Besides, most researchers concentrated on how to use Gabor filters to capture the continuity of ridge structures. On the contrast, we will apply Gabor filters to detect the discontinuity of ridge structures. Therefore we will develop a low-resolution and fewer steps approach to extract minutiae by using Gabor filters in this paper.

Secure information retrieval and reveal for mobile apparatus based on 2D Barcode digital signature

This paper introduces the mobile apparatus must make their best tradeoffs for the innate limitations of the physical properties and will benefit from the cloud services via wireless broadband access. Then a secure information retrieval and reveal framework based on two-dimensional barcode digital signature scheme is proposed for mobile apparatus. In addition to the secure protocol with cipher retrieval and barcode reveal presented, a case study is also presented to demonstrate how the framework is feasible and applicable especially for mobile commerce.

Singular Point Detection in Fingerprint Images by a Bank of Discrete Fourier Filters

Singular point detection is a critical process for both fingerprint matching and fingerprint classification. The process of singular points detection must be fast and robust; otherwise, the performance of the whole fingerprint recognition system would be influenced heavily. In this paper, we will combine the advantages of Poincaré index and fast Fourier transform to develop a fast and robust approach for singular point detection and to avoid the time-consuming problem of Gabor transform-based approach.

Fuzzy objective functions for robust pattern recognition

In this paper, we consider the issue of fuzzy objective functions when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Based on the proposed robust objective functions, algorithms for clustering are implemented. Artificially generated data are used for comparison.