Abstract— In order to verify tongueprint images, three approaches for texture analyses were considered and their performances are compared. They are wavelet transform, Gabor filter, and spectral analysis. In all approaches, six statistical measures are applied to the processed images to extract features. They are the mean, standard deviation, smoothness, third moment, uniformity, and entropy. Finally, k-nearest neighbor algorithm (k-NN) is used to classify tongue textures for verification purposes. The obtained recognition rates show that features extracted from wavelet analysis allow achieving the highest accuracy (92%) among the other approaches. On the other hand, features extracted from spectral images lead to the lowest recognition rate (75%). Features extracted from Gabor filter banks obtained 83%. Therefore, we conclude that wavelet-based features outperform Gabor and spectral-based features employed in the literature.

Index Terms— Tongueprints, wavelets transform, Gabor filter, Spectral analysis, k-NN.

Cite: Salim Lahmiri, "Recognition of Tongueprint Textures for Personal Authentication: A Wavelet Approach," Journal of Advances in Information Technology, Vol. 3, No. 3, pp. 168-175, August, 2012.doi:10.4304/jait.3.3.168-175