340 research outputs found

    Face Liveness Detection for Biometric Antispoofing Applications using Color Texture and Distortion Analysis Features

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    Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach

    Resilience of Luminance based Liveness Tests under Attacks with Processed Imposter Images

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    Liveness tests are techniques employed by face recognition authentication systems, aiming at verifying that a live face rather than a photo is standing in front of the system camera. In this paper, we study the resilience of a standard liveness test under imposter photo attacks, under the additional assumption that the photos used in the attack may have been processed by common image processing operations such as sharpening, smoothing and corruption with salt and pepper noise. The results verify and quantify the claim that this type of liveness tests rely on the imposter photo images being less sharp than live face images

    Designing a facial spoofing database for processed image attacks

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    Face recognition systems are used for user authentication in everyday applications such as logging into a laptop or smartphone without need to memorize a password. However, they are still vulnerable to spoofing attacks, as for example when an imposter gains access to a system by holding a printed photo of the rightful user in front of the camera. In this paper we are concerned with the design of face image databases for evaluating the performance of anti-spoofing algorithms against such attacks. We present a new database, supporting testing against an enhancement of the attack, where the imposter processes the stolen image before printing it. By testing a standard antispoofing algorithm on the new database we show a significant decrease in its performance and, as a simple remedy to this problem, we propose the inclusion of processed imposter images into the training set
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