Robust Laser Speckle Authentication System Through Data Mining Techniques

This paper proposes a speckle image recognition method using data mining techniques to ensure speckle identification system feasible for authentication. This is an interdisciplinary method that integrates the researches of optics, data mining, and image processing. Because objects have unique but imperfect surfaces, their laser speckle is capable of providing suitable identifiable features for authentication. In our method, matching points among speckle images acquired from one plastic card are extracted by scale-invariant feature transform (SIFT). The spatial relations among the matching points are then transformed to 9 direction lower triangular (9DLT) representations. Then, the Apriori algorithm mines frequent patterns so a useful association rule is obtained as the feature to identify the similarity between each of the speckle images for the purpose of authenticity verification. The proposed method is especially robust in the cases of card displacement and luminance change resulted from laser attenuation. Experimental results show that the proposed method has promising results and outperforms existing methods in identification accuracy.