Fingerprint Liveness Detection Using Handcrafted Feature Descriptors and Neural Network

Fingerprint recognition is commonly used to verify a user’s identity. However, the fingerprint recognition systems in use today can be vulnerable to attacks. For example, some artificial fingerprints can spoof fingerprint recognition systems and use identity information to obtain personal information. For security reasons, fingerprint liveness detectors should be robust to attacks using various materials. As a result, we propose a fingerprint recognition method that uses multi-radius local binary pattern (Multi-radius LBP) and local phase quantization (LPQ) as local texture descriptors and wavelet transforms to remove noise. Classification results are obtained using a multi-layer perceptron (MLP) classifier. This study confirms that the proposed method is useful to improve the average fingerprint liveness detection tasks. On LivDet 2015 dataset, the proposed method achieves 6.73% Average Error Rate(AER).