Wavelet based palmprint recognition

Palmprint is a new biometric method to recognize a person. The features in a palmprint include principal lines, wrinkles and ridges, etc. Line structure feature, which includes principal lines and wrinkles, is one of the most popular methods in palmprint recognition. However, the line structure feature does not contain the thickness and width information of principal lines and wrinkles, which are very important to discriminate palmprints. Ridges are not included in line structure feature either. So these methods cannot distinguish different palmprints with similar line structure. Furthermore, the line extraction is a difficult task. The fact that principal lines, wrinkles and ridges have different resolutions motivates us to analyze the palmprint using multi-resolution analysis method. A novel palmprint feature, named wavelet energy features, is defined employing wavelet, which is a powerful tool of multi-resolution analysis, in this paper. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in several directions at different wavelet decomposition level (scale), so its ability to discriminate palms is very strong. Easiness to compute is another virtue of WEF. The very high recognition rates obtained in experiments shows the effect of the proposed method.