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HyFiPAD: a hybrid approach for fingerprint presentation attack detection using local and adaptive image features

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Abstract

With the pervasiveness of secured biometric authentication applications, the fingerprint-based identification system has fascinated much attention recently. However, the major detriment is their recognition sensors are vulnerable to presentation or spoofing attacks from fake fingerprint artifacts. To resolve these issues, a viable anti-deception countermeasure known as presentation attack detection (PAD) mechanism is developed. As handcrafted feature-based classification techniques exhibit encouraging results in computer vision, they are widely employed in fingerprint spoof detection. Notably, the single-feature-based techniques do not perform uniformly over different spoofing and sensing technologies. In this research work, we expound a new hybrid fingerprint presentation attack detection approach (HyFiPAD) that discriminates live and fake fingerprints using majority voting ensemble build on three local and adaptive textural image features. We propose a new descriptor (i.e., a variant of LBP) which is termed as Local Adaptive Binary Pattern (LABP). Thus, the notion of proposed LABP is used to extract more detailed micro-textural features from the fingerprint images. Our LABP features are combined with an existing Complete Local Binary Pattern (CLBP) descriptor to learn two respective SVM classifiers and additionally a sequential model is trained with the manually extracted Binary Statistical Image Features (BSIF). The experiments are performed on benchmark anti-spoofing datasets namely; LivDet 2009, LivDet 2011, LivDet 2013, and LivDet 2015, where an average classification error rate (ACER) of 4.11, 3.19, 2.88, and 2.97% is, respectively, achieved. The overall experimental analysis of the HyFiPAD demonstrates superiority against majority of the state-of-the-art methods. In addition, the proposed technique yields a promising performance on cross-database and cross-sensor liveness detection tests, claiming good generalization capability.

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Acknowledgements

The LivDet datasets employed in this research are kindly provided by Clarkson University for which authors express their gratitude.

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  1. Department of Computer Science and Information Technology, Central University of Jammu, Samba, Jammu and Kashmir, 181143, India

    Deepika Sharma & Arvind Selwal

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  1. Deepika Sharma
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Table 11 A summary of state-of-the-art fingerprint PAD techniques
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Sharma, D., Selwal, A. HyFiPAD: a hybrid approach for fingerprint presentation attack detection using local and adaptive image features. Vis Comput 38, 2999–3025 (2022). https://doi.org/10.1007/s00371-021-02173-8

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