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    Volume 46, Issue 6 (April 2018)

    Textural and Geometrical Features Based Approach for Identification of Individuals Using Palmprint and Hand Shape Images from Multiple Multimodal Datasets

    (Received 5 December 2016; accepted 28 July 2017)

    Published Online: 09 April 2018

    CODEN: JTEVAB

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    Abstract

    Identification and classification of biometrics are important research areas in the field of image processing and pattern recognition. Biometrics are the measurement and statistical analysis of physiological and behavioral characteristics of humans. A wide variety of biometric modalities are available, with unimodal biometrics suffering from several factors. The proposed research is novel because it uses a single image of a hand in order to extract a variety of unique characteristics, like hand shape and the palmprint associated with individual hands. Moreover, it obtains higher accuracy with minimum effort. We have chosen the multimodal biometrics, i.e., palmprint and hand shape, from three datasets, i.e., PolyU Palmprint Database, GPDS Hand Database, and the Bosphorus Hand Database, for a total of 1,072 images. There are 302 textural features found in the palmprint images, and 12 geometrical features are extracted from the hand images. Classification models include Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (IBk), Decision Tree, Random Tree, Random Forest, and Bagging. The train and test method is used to evaluate the performance of different classifiers. It is observed that Naïve Bayes, SVM, IBk, and Random Tree models result in classification accuracy of 99.44 % with palmprint images using the 302 textural features over the combined dataset. After feature reduction, similar accuracy is achieved with the top ten, and even with the top five, features. For geometrical features, an accuracy of 99.81 % is achieved with the hand images using Naïve Bayes, SVM, IBk, and Random Tree.

    Author Information:

    Shaukat, Anum
    Department of Computer Science, Lahore College for Women University, Lahore,

    Farhan, Saima
    Department of Computer Science, Lahore College for Women University, Lahore,

    Fahiem, Muhammad Abuzar
    Department of Computer Science, Lahore College for Women University, Lahore,

    Tauseef, Huma
    Department of Computer Science, Lahore College for Women University, Lahore,

    Tahir, Fahima
    Department of Computer Science, Lahore College for Women University, Lahore,

    Usman, Ghousia
    Department of Computer Science, Lahore College for Women University, Lahore,


    Stock #: JTE20160625

    ISSN:0090-3973

    DOI: 10.1520/JTE20160625

    Author
    Title Textural and Geometrical Features Based Approach for Identification of Individuals Using Palmprint and Hand Shape Images from Multiple Multimodal Datasets
    Symposium ,
    Committee E12