1,720,977 research outputs found
Fingerprint liveness detection by local phase quantization
No full textFingerprint liveness detection consists in verifying if an input fingerprint image, acquired by a fingerprint verification system, belongs to a genuine user or is an artificial replica. Although several hardware- and software-based approaches have been proposed so far, this issue still remains unsolved due to the very high difficulty in finding effective features for detecting the fingerprint liveness. In this paper, we present a novel features set, based on the local phase quantization (LPQ) of fingerprint images. LPQ method is well-known for being insensitive to blurring effects, thus we believe it could be useful for detecting the differences between an alive and a fake fingerprint, due to the loss of information which may occur during the replica fabrication process. The method is tested on the four data sets of the Second International Fingerprint Liveness Detection Competition, and shows promising and competitive results with other state-of-the-art features sets. © 2012 ICPR Org Committee
Fingerprint presentation attacks detection based on the user-specific effect
No full textThe similarities among different acquisitions of the same fingerprint have never been taken into account, so far, in the feature space designed to detect fingerprint presentation attacks. Actually, the existence of such resemblances has only been shown in a recent work where the authors have been able to describe what they called the "user-specific effect". We present in this paper a first attempt to take advantage of this in order to improve the performance of a FPAD system. In particular, we conceived a binary code of three bits aimed to "detect" such effect. Coupled with a classifier trained according to the standard protocol followed, for example, in the LivDet competition, this approach allowed us to get a better accuracy compared to that obtained with the "generic users" classifier alone
Experimental results on the feature-level fusion of multiple fingerprint liveness detection algorithms
No full textThe aim of fingerprint liveness detection is to detect if a fingerprint image, sensed by an electronic device, belongs to an alive fingertip or to an artificial replica of it. It is well-known that a fingerprint can be replicated and standard electronic sensors cannot distinguish between a replica and an alive fingerprint image. Accordingly, several coun-termeasures in terms of fingerprint liveness detection algorithms have been proposed, but their performance is not yet acceptable. However, no works studied the possibility of combining different feature sets, thus exploiting the eventual complementarity among them. In this paper, we show some preliminary experiments on feature-level fusion of several algorithms, including a novel feature set proposed by the authors. Experiments are carried out on the datasets available at Second International Fingerprint Liveness Detection Competition (LivDet 2011). Reported results clearly show that multiple feature sets allow improving the liveness detection performance. Copyright 2012 ACM
Fingerprint liveness detection by local phase quantization
No full textFingerprint liveness detection consists in verifying if an input fingerprint image, acquired by a fingerprint verification system, belongs to a genuine user or is an artificial replica. Although several hardware- and software-based approaches have been proposed so far, this issue still remains unsolved due to the very high difficulty in finding effective features for detecting the fingerprint liveness. In this paper, we present a novel features set, based on the local phase quantization (LPQ) of fingerprint images. LPQ method is well-known for being insensitive to blurring effects, thus we believe it could be useful for detecting the differences between an alive and a fake fingerprint, due to the loss of information which may occur during the replica fabrication process. The method is tested on the four data sets of the Second International Fingerprint Liveness Detection Competition, and shows promising and competitive results with other state-of-the-art features sets. © 2012 ICPR Org Committee
Fingerprint Liveness Detection using Binarized Statistical Image Features
No full textRecent experiments, reported in the third edition of Fingerprint Liveness Detection competition (LivDet 2013), have clearly shown that fingerprint liveness detection is a very difficult and challenging task. Although the number of approaches is large, none of them can be claimed as able to detect liveness of fingerprint traits with an acceptable error rate. In our opinion, in order to investigate at which extent this error can be reduced, novel feature sets must be proposed, and, eventually, integrated with existing ones. In this paper, a novel fingerprint liveness descriptor named 'BSIF' is described, which, similarly to Local Binary Pattern and Local Phase Quantization-based representations, encodes the local fingerprint texture on a feature vector. Experimental results on LivDet 2011 data sets appear to be encouraging and make this descriptor worth of further investigations. © 2013 IEEE
Exploiting the golden ratio on human faces for head-pose estimation
No full textIn this paper, a novel method for automatic head pose estimation is presented. This is based on a geometrical model of the head, in which basic features for estimating the pose consist in eyes and nose coordinates only. Worth noting, the majority of state-of-the-art approaches requires at least five features. The novelty of our work is the exploitation of the Vitruvian man's proportions and the related "Golden Ratio". The "Vitruvian man" is the well-known master-work by Leonardo Da Vinci, never used for automatic head pose estimation. Proposed method is compared by experiments with state-of-the-art ones, and shows a competitive performance although its simplicity and its low computational complexity. © 2013 Springer-Verlag
On the interoperability of capture devices in fingerprint presentation attacks detection
No full textA presentation attack consists in submitting to the fingerprint capture device an artificial replica of the finger of the targeted client. If the sensor is not equipped with an appropriate algorithm aimed to detect the fingerprint spoof, the system processes the obtained image as a one belonging to a real fingerprint. In order to face this problem, several presentation attacks detection (PAD) algorithms have been proposed so far. Current methods heavily rely on features extracted from a large data set of fake and real fingerprint images, and an appropriate classifier trained with such data to distinguish between live (real) and fake (spoof) fingerprint images. Building such data set requires a significant effort for fabricating samples of fake fingerprints, with the most effective materials used to circumvent the sensor. Interesting and promising results have been obtained, but they also suggest that the PAD is tailored on the particular sensor. Small and significant differences also occur when a novel version of the same sensor is released, and this may affect the PAD. Therefore, making a PAD interoperable is among the main current issues when considering fingerprints as the first level of protection and security of logical or physical resources. This paper is a first attempt to assess at which extent the sensor interoperability can be an issue for fingerprint PADs and to eventually propose a solution to this limitation. In particular, textural features will be under focus and a feature space transformation method based on the least square is proposed
On combining edge detection methods for improving BSIF based facial recognition performances
No full textLighting variation is a major challenge for an automatic face recognition system. In order to overcome this problem, many methods have been proposed. Most of them try to extract features invariant to illumination changes or to reduce illumination changes in a pre-processing step and to extract features for recognition. In this paper, we present a procedure similar to the latter where the two steps are complementary. In the pre-processing step we deal with the illumination changes and in the features extraction step we use the BSIF (Binarized Statistical Image Features), a recently proposed textural algorithm. In our opinion, a method capable of reducing the lighting variations is ideal for an algorithm like the BSIF. The performance of our system has been tested on the FRGC dataset and the presented results show the validity of our approach
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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