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An intelligent approach for fingerprint presentation attack detection using ensemble learning with improved local image features

  • 1200: Machine Vision Theory and Applications for Cyber Physical Systems
  • Published:
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Abstract

The fingerprint-based authentication systems are being extensively deployed as security tool for providing access to the critical Cyber Physical Systems (CPS). However, this rapid relocation to fingerprint-based recognition poses many security concerns that are primarily due to presentation or spoofing attacks on sensor module. To alleviate these attacks, presentation attack detection (PAD) mechanisms are developed that mainly rely on micro-textural properties of the fingerprint images. In this paper, we expound a novel intelligent fingerprint PAD (IFPAD) approach for securing typical CPS that exploits two micro-textural features from an image. We propose a new Local Adaptive Binary Patterns (LABP) descriptor that extracts more refined local information by using dynamically adapted threshold. Moreover, to augment more discriminative power, the Uniform Local Binary patterns (ULBP) descriptor is coalesced with our LABP feature. To yield better detection accuracy, an Adaptive Boosting (AdaBoost) ensemble is created on two base estimators using Support Vector Machine (SVM) that learns LABP and ULBP features. The IFPAD is experimentally evaluated on three benchmark datasets; LivDet 2009, LivDet 2011, and LivDet 2013 where an average classification error rate (ACER) of 4.23%, 3.83% and 3.57% is achieved respectively. In addition, the promising performance of IFPAD in case of cross-database and cross-sensor scenario confirms its good generalization capabilities in unknown environment. Besides, overall analysis of the proposed IFPAD demonstrates superiority as compared to both handcrafted and deep learning-based state-of-the-art PAD methods.

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Acknowledgements

Authors are thankful to the Clarkson University for providing the LivDet datasets that are employed in this research.

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  1. Department of Computer Science & Information Technology, Central University of Jammu, Jammu, J&K, India, 181143

    Deepika Sharma & Arvind Selwal

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  1. Deepika Sharma
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Correspondence to Deepika Sharma.

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Sharma, D., Selwal, A. An intelligent approach for fingerprint presentation attack detection using ensemble learning with improved local image features. Multimed Tools Appl 81, 22129–22161 (2022). https://doi.org/10.1007/s11042-021-11254-8

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