Fusing Multi-techniques Based on LDA-CCA and Their Application in Palmprint Identification System

In this paper, we investigate an efficient palmprint texture modeling method that incorporates a robust analysis based on fusing multiple information. In fact, a single descriptor alone may not achieve a high accuracy in palmprint biometric system. Hence, we propose the fusion of various information features extracted by the different descriptors, such as the fractal and the Multi-fractal techniques which produce a robustness to face the numerous challenging and variation of palmprint in unconstrained environments. To increase the performance of palmprint biometric systems, information fusion is proposed as a key phase in multi-characteristic systems. The obtained information can be combined at different levels, i.e., at the feature level, the score level or the decision level. Nevertheless, the feature level fusion is considered more effective than both the matching score and the classifier decision levels, thanks to a feature vector set which contains more and richer information about the input palmprint image. In order to improve the discriminating texture information, our proposed method extracts the fractal dimension features from the preprocessed palmprint images and fuses them with the Multi-fractal dimension features using the Canonical Correlation Analysis (CCA) incorporating the Linear Discriminant Analysis (LDA) in order to reduce the feature dimensionality for each feature set. To demonstrate the feasibility and effectiveness of our proposed method, we performed the experimental results on two benchmark datasets. These results outperform other well-known state of the art methods and produce promising recognition rates by achieving 96.02% for PolyU-Palmprint database and 97.00% for CASIA-Palmprint database.