Anas Husseis

Bio: Anas Husseis is an academic researcher from Carlos III Health Institute. The author has contributed to research in topics: Fingerprint (computing) & Fingerprint recognition. The author has an hindex of 4, co-authored 8 publications receiving 40 citations. Previous affiliations of Anas Husseis include Complutense University of Madrid & University of Paris.

A Survey in Presentation Attack and Presentation Attack Detection

Abstract: Biometric-based recognition has been replacing conventional recognition methods in security systems. Modern electronic devices such as smartphones and online services have been employing biometric systems because of their security, acceptability, and usability. However, the wide deployment of Biometrics raises security concerns including attacks that aim to interfere with a system’s operation. This paper provides a review of potential threats which may affect biometric systems’ security, particularly, Presentation Attack (PA). A general taxonomy of presentation attacks is proposed to cover different biometric modalities considering the attacker’s intention and the presentation instrument. Moreover, Presentation Attack Detection (PAD) mechanisms that aim to eliminate or mitigate those attacks are also taxonomized. The taxonomy analyzes PAD mechanisms wherein the biometric trait pattern is considered to classify PAD methods. A state of the art study has been carried out to investigate PA and PAD for six biological and behavioral modalities.

Presentation Attack Detection Evaluation on Mobile Devices: Simplest Approach for Capturing and Lifting a Latent Fingerprint

Abstract: Given that nowadays we store increasing sensitive data on our mobile devices (documents, photos, bank accounts, etc.), it is crucial to know how secure the protection of the phone really is. Most new smartphones include an embedded fingerprint sensor due to its comfort of use and security. In the last decades, many studies and tests have shown that it is possible to steal a person's fingerprint and reproduce it, with the intention of impersonating them. This has become a bigger problem as the adoption of fingerprint sensor cell phones have become mainstream. Thus, it is important to know how easy it really is for anyone to capture a latent fingerprint. For this work, we searched for the easiest process to steal a latent fingerprint from a phone screen. In 2013, the Chaos Computer Club proved that this was possible using a desktop scanner and image editing programs. Nowadays, smartphones have high quality cameras and document scanning apps that do exactly the image processing needed to obtain a fingerprint from a screen, making it very easy for attackers to steal someone's identity. Thus, the approach used in this work is taking a picture of the genuine subject's screen with a document scanner app that can be downloaded for free for the phone, and let it do the common processing for documents, which is one tap away for the attacker. Then, a PAD (Presentation Attack Detection) evaluation was made to check how many attempts were successful using this technique. This evaluation was performed on 5 smartphones, using 15 capture subjects and making a total of 17,100 attack attempts. All smartphones were successfully attacked using this simple technique with at least 3 out of 4 artefact species.

Revisited histogram equalization as HDR images tone mapping operators

Abstract: High Dynamic Range (HDR) image provides higher perceptual quality such that it appears considerably more realistic and attractive for the human observer. Since most of current screens are Low Dynamic Range (LDR) screens, lots of researches have been proposed to design tone mapping algorithms converting the HDR images into a range that is suitable to display these tone mapped images on standard LDR screens. For this purpose, this paper first investigates the pixels distribution in the HDR image through the study of various traditional Histogram Equalization (HE) algorithms which were originally developed to improve the visual quality of LDR images such as the contrast enhancement of the latter. Moreover, a modification of the Histogram Adjustment based Linear to Equalized Quantizer (HALEQ), developed for HDR images, is proposed. Simulation results show that the proposed modification preserves more details than the original version of the algorithm in most parts of the HDR image.

Attacking a Smartphone Biometric Fingerprint System: A Novice's Approach

Abstract: Biometric systems on mobile devices are an increasingly ubiquitous method for identity verification. The majority of contemporary devices have an embedded fingerprint sensor which may be used for a variety of transactions including unlock a device or sanction a payment. In this study we explore how easy it is to successfully attack a fingerprint system using a fake finger manufactured from commonly available materials. Importantly our attackers were novices to producing the fingers and were also constrained by time. Our study shows the relative ease that modern devices can be attacked and the material combinations that lead to these attacks.

Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features

Abstract: This paper presents a novel mechanism for fingerprint dynamic presentation attack detection. We utilize five spatio-temporal feature extractors to efficiently eliminate and mitigate different presentation attack species. The feature extractors are selected such that the fingerprint ridge/valley pattern is consolidated with the temporal variations within the pattern in fingerprint videos. An SVM classification scheme, with a second degree polynomial kernel, is used in our presentation attack detection subsystem to classify bona fide and attack presentations. The experiment protocol and evaluation are conducted following the ISO/IEC 30107-3:2017 standard. Our proposed approach demonstrates efficient capability of detecting presentation attacks with significantly low BPCER where BPCER is 1.11% for an optical sensor and 3.89% for a thermal sensor at 5% APCER for both.

Anti-spoofing method for fingerprint recognition using patch based deep learning machine

Abstract: Today’s with increasing identity theft, biometric systems based on fingerprints have a growing importance in protection and access restrictions. Malicious users violate them by presenting fabricated attempts. For example, artificial fingerprints constructed by gelatin, Play-Doh and Silicone molds may be misused for access and identity fraud by forgers to clone fingerprints. This process is called spoofing. To detect such forgeries, some existing methods using handcrafted descriptors have been implemented for assuring user presence. Most of them give low accuracy rates in recognition. The proposed method used Discriminative Restricted Boltzmann Machines to recognize fingerprints accurately against fabricated materials used for spoofing.

Recent advancements in finger vein recognition technology: Methodology, challenges and opportunities

Abstract: Finger vein recognition biometric trait is a significant biometric modality that is considered more secure, reliable, and emerging. This article presents a review to focus on the recent research landscape in biometric finger vein recognition systems. This article focuses on manuscripts related to keywords ‘Finger Vein Authentication System’, ‘Anti-spoofing or Presentation Attack Detection’, ‘Multimodal Biometric Finger Vein Authentication’ and their variations in four main digital research libraries such as IEEE Xplore, Springer, ACM, and Science Direct. The final set of articles is divided into three main categories: Deep Learning (DL) based finger vein recognition, Presentation Attack Detection (PAD), and Multimodal-based finger vein authentication system. Deep learning-based finger vein recognition techniques are further sub-divided into pre-processing (Quality assessment and enhancement) based, feature extraction based, and feature extraction and recognition based schemes. Presentation attack detection methods are sub-divided into software-based and hardware-based approaches. Multimodal-based finger vein biometric system is sub-categorized into feature level fusion, matching level fusion, and hybrid fusion methods. The authors have studied the problem of the recent algorithm and their solution related to finger vein biometric system from the recent literature. Performance analysis and selected the best promising research work from the mentioned studies are also presented. Finally, open challenges, opportunities, and suggested solutions related to deep learning, PAD, and Multimodal based finger vein recognition systems have been discussed in the discussion section. This work would be helpful to the developers, company users, researchers, and readers to get insight into the importance of such technology and the recent problem faced by finger vein authentication technology with respect to deep learning and multimodal systems.

Biometric System And Data Analysis Design Evaluation And Data Mining

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Face Liveness Detection Competition (LivDet-Face) - 2021

Abstract: Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions against the LivDet-Face winners.

Tone-Mapping Using Perceptual-Quantizer and Image Histogram

Abstract: A new tone-mapping algorithm is presented for visualization of high dynamic range (HDR) images on low dynamic range (LDR) displays. In the first step, the real-world pixel intensities of the HDR image are transformed to a perceptual domain using the perceptual-quantizer (PQ). This is followed by construction of the histogram of the luminance channel. Tone-mapping curve is generated from the cumulative histogram. It is known that histogram-based tone-mapping approaches can lead to excessive stretching of contrast in highly populated bins, whereas the pixels in sparse bins can suffer from excessive compression of contrast. We handle these issues by restricting the pixel counts in the histogram to remain below a defined limit, determined by a uniform distribution model. The proposed method is compared with state-of-the-art algorithms, using some well-known metrics that quantify the quality of tone-mapped images, and is found to have the best performance.