Transfer of verification dataDate:2021-09-17
Finger vein Liveness Detection Using Motion Magnification
Fingerprint verification is one of the most reliable personal identification methods. It plays a very important role in forensic and civilian applications. In this paper we propose a simple and effective approach for fingerprint liveness detection based on the wavelet analysis of the finger tip surface texture. In present situation, manual fingerprint verification is so tedious, time-consuming, expensive and incapable of meeting today's increasing performance requirements. Wavelet transform which has wide range of applications such as image compression, denoising noisy data, texture classification, etc., is used for fingerprint verification. Experimental results show that our method can successfully differentiate live finger tips from fake finger tips made of most commonly used material in fingerprint spoofing.Keywords - Denoising, Fingerprint, Liveness, spoofing, Wavelet.I. INTRODUCTIONRecent research works is really from a live finger tip or not by acquiring and analyzing accessorial finger tip information other the minutiae is called liveness detection. Static properties (e.g. temperature; conductivity) and dynamic behaviors (e.g. skin deformation; perspiration) of live finger tips have been extensively studied in fingerprint liveness detection research . All of the existing approaches are believed to have certain limitations because the properties used are either unstable or not universal enough . This Letter proposes a new fingerprint liveness detection approach based on analyzing a more robust and intrinsic property of finger tips - surface coarseness.
Current state-of-the-art dual camera-based face liveness detection methods utilize either hand-crafted features, such as disparity, or deep texture features to classify a live face and face Presentation Attack (PA). However, these approaches limit the effectiveness of classifiers, particularly deep Convolutional Neural Networks (CNN) to unknown face PA in adverse scenarios. In contrast to these approaches, in this paper, we show that supervising a deep CNN classifier by learning disparity features using the existing CNN layers improves the performance and robustness of CNN to unknown types of face PA. For this purpose, we propose to supervise a CNN classifier by introducing a disparity layer within CNN to learn the dynamic disparity-maps. Subsequently, the rest of the convolutional layers, following the disparity layer, in the CNN are supervised using the learned dynamic disparity-maps for face liveness detection. We further propose a new video-based stereo face anti-spoofing database with various face PA and different imaging qualities. Experiments on the proposed stereo face anti-spoofing database are performed using various test case scenarios. The experimental results indicate that our proposed system shows promising performance and has good generalization ability.
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