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Palmprint recognition system based on deep region of interest features with the aid of hybrid approach

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Abstract

Palmprint recognition system is a biometric technology, which is promising to have a high precision. This system has started to attract the attention of researchers, especially with the emergence of deep learning techniques in recent years. In this study, a deep learning and machine learning-based hybrid approach has been recommended to recognize palmprint images automatically via region of interest (ROI) features. The proposed work consists of several stages, respectively. In the first stage, the raw images have been collected from the PolyU database and preprocessing operations have been implemented in order to determine ROI areas. In the second stage, deep ROI features have been extracted from the preprocessed images with the aid of deep learning technique. In the last stage, the obtained deep features have been classified by employing a hybrid deep convolutional neural network and support vector machine models. Finally, it has been observed that the overall accuracy of the proposed system has achieved very successful results as 99.72% via hybrid approach. Moreover, very low execution time has been observed for whole process of the proposed system with 0.10 s.

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Data is available http://www.comp.polyu.edu.hk/~biometrics/

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Authors and Affiliations

  1. Department of Computer Programming, Vocational School, Mardin Artuklu University, 47500, Mardin, Turkey

    Ömer Türk

  2. Department of Computer Engineering, Batman University, 72060, Batman, Turkey

    Abidin Çalışkan

  3. Department of Electrical and Electronics Engineering, Batman University, 72060, Batman, Turkey

    Emrullah Acar

  4. Department of Computer Engineering, Fırat University, 23100, Elazığ, Turkey

    Burhan Ergen

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  1. Ömer Türk
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  2. Abidin Çalışkan
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  3. Emrullah Acar
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Contributions

ÖT contributed to conceptualization, methodology, software, writing—original draft preparation, reviewing and editing, and validation. AÇ contributed to conceptualization, methodology, software, writing—original draft preparation, reviewing and editing, and validation. EA contributed to conceptualization, methodology, software, writing—original draft preparation, reviewing and editing, and validation. BE contributed to reviewing and editing.

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Correspondence to Ömer Türk.

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Türk, Ö., Çalışkan, A., Acar, E. et al. Palmprint recognition system based on deep region of interest features with the aid of hybrid approach. SIViP 17, 3837–3845 (2023). https://doi.org/10.1007/s11760-023-02612-0

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