A Novel Approach of Palmprint Recognition using PCA, DWT and Random Forest Classifier

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.4 · Issue 10

Published

May 2016

Access

Open Access

Licence

CC BY-NC-SA 4.0

Yogesh Dhanotiya Neeraj Shrivastava

Palmprint recognition being one of the important aspects of biometric technology is one of the most reliable and successful identification methods. Palmprint is an important complement and reliable biometric that can be used for identity verification because it is stable and unique for every individual. This paper is divided into two phases, training phase and testing phase. In training phase there are four sub processes; pre-processing, feature extraction and dimensionality reduction. Pre-processing is done with the help of image resizing and RGB to Gray conversion. For feature extraction, we have used Gabor Filter and Discrete Wavelet Transform (DWT). Principal Component analysis (PCA) is used for dimensionality reduction. The extracted features are then stored in database. In the testing phase the same process is done up to the PCA and then the similarity measure with database is done. Random Forest Classifier is used for similarity measure. The MATLAB image processing tool box is used to implement proposed Palmprint recognition system.

Yogesh Dhanotiya, Neeraj Shrivastava (2016). A Novel Approach of Palmprint Recognition using PCA, DWT and Random Forest Classifier. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.4, Issue 10. ISSN: 2319-4863.

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