Recognition of Tongueprint Textures for Personal Authentication: A Wavelet Approach

Abstract

<p>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 neighbour algorithm (<em>k</em>-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.</p

People Recognition via Tongue Print Using Deep and Machine Learning

“…The k-nearest neighbor (k-NN) algorithm was first presented by [18] as a nonparametric technique and allocates query data to the class to which most of its k-nearest neighbors belong. For instance, the k-NN method classifies data independently without requiring an explicit model.…”

People identification via tongue print using fine-tuning deep learning

“…The actual accept rate was 93.3%, while the false accept rate was 2.9%. Lahmiri [11] proposed three different texture analysis methods. They are the Gabor filter, the wavelet transform, spectral analysis, and finding the K-nearest neighbor algorithm (KNN).…”