Won-Churl Jang

Fingerprint image enhancement based on a half gabor filter

The performance of a fingerprint recognition system relies on the quality of the input fingerprint images. Several researches have been studied on the enhancement of fingerprint images for fingerprint recognition. The representative enhancement is the adaptive filtering method based on Gabor filter (GF). However, this method is computationally expensive due to the large mask size of GF. In this paper, we propose a half Gabor filter (HGF), which is suitable for fast implementation in spatial domain. The HGF is a modified filter which preserves the frequency property of a GF and reduces the mask size of the GF. Compared with the GF, the HGF not only reduces the processing time approximately by 41% but also enhances the fingerprint image which is as reliable as the GF.

A real-time image selection algorithm: fingerprint recognition using mobile devices with embedded camera

This paper proposes a real-time image selection algorithm for fingerprint recognition system, which uses the embedded camera of the mobile device. In general, auto-focusing algorithms of the camera system use the gradient measures to estimate high-frequency components of an image. In mobile device, images are greatly affected by environmental light sources. Therefore, obtained image might not qualify for the fingerprint recognition system, even when the image is focused. Consequently, image should be investigated whether it is usable or not. Variance-modified-Laplacian of Gaussian (VMLOG) and image quality index (QI) proposed in this paper solve such problem. VMLOG considers high-frequency component and repeatable patterns of ridges. Experimental results shows that the processing time of the proposed algorithm is enough fast to be adapted in real time system as like mobile device and the proposed algorithm selects exactly a recognizable image.