1,720,985 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.Fingerprint 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
Improving Fingerprint Presentation Attack Detection by an Approach Integrated Into the Personal Verification Stage
No full textPresentation Attack Detection (PAD) systems are usually designed independently of the fingerprint verification system. While this can be acceptable for use cases where specific user templates are not predetermined, it represents a missed opportunity to enhance security in scenarios where integrating PAD with the fingerprint verification system could significantly leverage users’ templates, which are the real target of a potential presentation attack. This does not mean that a PAD should be specifically designed for such users; that would imply the availability of many enrolled users’ PAI and, consequently, complexity, time, and cost increase. On the contrary, we propose to equip a basic PAD, designed according to the state of the art, with an innovative add-on module called the Closeness Binary Code (CC) module. The term “closeness” refers to a peculiar property of the bona fide-related features: in an Euclidean feature space, genuine fingerprints tend to cluster in a specific pattern. First, samples from the same finger are close to each other, then samples from other fingers of the same user and finally, samples from fingers of other users. This property is statistically verified in our previous publication, and further confirmed in this paper. It is independent of the user population and the feature set class, which can be handcrafted or deep network-based (embeddings). Therefore, the add-on can be designed without the need for the targeted user samples; moreover, it exploits her/his samples’ “closeness” property during the verification stage. Extensive experiments on benchmark datasets and state-of-the-art PAD methods confirm the benefits of the proposed add-on, which can be easily coupled with the main PAD module integrated into the fingerprint verification system
On the interoperability of capture devices in fingerprint presentation attacks detection
Full text linkA 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
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 investigation
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
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
In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment
Full text linkAn early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on manual counting of fruits or flowers by workers is a time consuming and expensive process and it is not feasible for large fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. In a typical image classification process, the task is not only to specify the presence or absence of a given object on a specific location, while counting how many objects are present in the scene. The success of these tasks largely depends on the availability of a large amount of training samples. This paper presents a detector of bunches of one fruit, grape, based on a deep convolutional neural network trained to detect vine bunches directly on the field. Experimental results show a 91% mean Average Precision
Unmanned aerial system plant protection products spraying performance evaluation on a vineyard
Full text linkIn the context of increasing global food demand and the urgent need for production processes optimization, plant protection products play a key role in safeguarding crops from insects, pests, and fungi, responsible of plant diseases proliferation and yield losses. Despite the inaccurate distribution of conventional aerial spraying performed by airplanes and helicopters, Unmanned Aerial Spraying Systems (UASSs) offer low health risks and operational cost solutions, preserving crops and soil from physical damage. This study explores the impact of UASS flight height (2 m and 2.5 m above ground level), speed (1 m s−1 and 1.5 m s−1), and position (over the canopy and the inter-row) on vineyard aerial spraying efficiency by analysing Water Sensitive Papers droplet coverage, density, and Number Median Diameter using a MATLAB script. Flight position factor, more than others, influenced the application results. The specific configuration of 2 m altitude, 1.5 m s−1 cruising speed, and inter-row positioning yielded the best results in terms of canopy coverage, minimizing off-target and ground dispersion, and represented the best setting to facilitate droplets penetration, reaching the lowest parts generally more affected from disease. Further research is needed to assess UASS aerial PPP distribution effectiveness and environmental impact in agriculture, crucial for technology implementation, especially in countries where aerial treatments are not yet permitted
User-specific effects in Fingerprint Presentation Attacks Detection: Insights for future research
No full textA fingerprint presentation attacks detector (FPAD) is designed to obtain a certain performance regardless of the targeted user population. However, two recent works on facial traits showed that a PAD system can exploit very useful information from the targeted user population. In this paper, we explored the existence of that kind of information in fingerprints when textural features are adopted. We show by experiments that such features embed not only intrinsic differences of the given fingerprint replica with respect to a generic live fingerprint, but also contains characteristics present in other fingers of the same user, and characteristics extracted directly from spoofs of the targeted fingerprint itself. These interesting evidences could lead to novel developments in the design of future FPADs
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