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This paper presents an improved fingerprint recognition system for overlapping fingerprint of two fingers. The developed system can automatically and precisely separate an overlapping fingerprint into two areas, an overlapping area and a non-overlapping area, by analyzing their orientation fields and complicities. A method being able to remove a misjudging core of the fingerprint from the overlapping area is also proposed in this paper. Moreover, a recursive correction algorithm and a restrictions relaxation labeling algorithm are applied to separate two fingerprints from the determined overlapping fingerprint area, and the Gabor filter is applied to enhance figure quality of the two separated fingerprints. After that, the VeriFinger 6.2 SDK is used to identify the fingerprints. In the experiments, our system uses the fingerprints from the two public databases. Receiver Operating Characteristic (ROC) and Cumulative Match Characteristic (CMC), are used to examine the proposed fingerprint recognition system. The average recognition rate of our system is 85.7%. The experiments results show the effectiveness of the proposed system.

Among all the fingerprint identification/verification systems, such as minutiae-based or filterbank-based fingerprint matching, the performance relies heavily on the quality of the input fingerprint images. In this paper, we propose an effective algorithm of fingerprint image enhancement, which can much improve the clarity and continuity of ridge structures based on the multiresolution analysis of global texture and local orientation by the wavelet transform. Experimental results show that the enhanced image quality by using the wavelet-based enhancement algorithm is much better than the other existing methods for improving the minutiae detection.

Several successful approaches to speech recognition have been proposed. Most of them involve time alignment which requires substantial computation and considerable memory storage. In this paper, we present a neuro-fuzzy approach to speech recognition without time alignment. This approach is a powerful method for selecting reference templates; therefore, considerable memory storage is alleviated. In addition, it greatly reduces substantial computation in the matching process because it obviates time alignment. Base on this approach, a Mandarin speech recognition system without time alignment is implemented. Two databases were utilized for verifying its performance. An encouraging experimental result confirms the effectiveness of the proposed neuro-fuzzy approach to speech recognition without time alignment.