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A Review: Face Liveness Detection


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Defending Against Voice Spoofing: A Robust Software-Based Liveness Detection System
Liveness detection in biometric systems has become an integral part of system viability, but it has innate disadvantages concerning implementation, situational suitability and acceptance. This article looks at the potential for combining liveness detection techniques with autonomous concepts to minimize, negate or even improve the original system. This is done by considering two potential areas within the autonomous system purview, autonomous architectures and the human nervous system paradigm. Within each there are a number of areas that could accept liveness detection incorporation and potentially improve each applicable subsystem. This article will cover an introduction into these topics and a discussion about their suitability.
In recent years, fingerprint authentication systems are convenient for us to verify the identity of the user by extracting and analysing these biometric features, so they have been rapidly developed in our daily life. However, current existing problem is that fingerprint authentication systems are vulnerable to spoofing attacks, such as artificial fake fingerprints. Moreover, the classification accuracy of traditional liveness detection methods for different sensors is not satisfactory. Therefore, in order to solve these spoofing attacks and enhance the classification performance for samples of different fingerprint sensors, a new software-based fingerprint liveness detection method, which is based on the multiscale wavelet transform and the rotaion-invarient local binary pattern (RILBP), was proposed in this paper. The fingerprint samples are derived from four different fingerprint sensors in LivDet 2011. Experimental results demonstrate that our method can detect the fingerprint liveness with higher classification performance compared with other methods of fingerprint liveness detection. 2018 Taiwan Academic Network Management Committee. All rights reserved.

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