1,155 research outputs found

    A Survey of Super-Resolution in Iris Biometrics With Evaluation of Dictionary-Learning

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    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches thus need to incorporate the specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an eigen-patches’ reconstruction method based on the principal component analysis eigen-transformation of local image patches. The structure of the iris is exploited by building a patch-position-dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded the high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators that were used to carry out biometric verification and identification experiments. The experimental results show that the proposed method significantly outperforms both the bilinear and bicubic interpolations at a very low resolution. The performance of a number of comparators attains an impressive equal error rate as low as 5% and a Top-1 accuracy of 77%–84% when considering the iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matchingThis work was supported by the EU COST Action under Grant IC1106. The work of F. Alonso-Fernandez and J. Bigun was supported in part by the Swedish Research Council, in part by the Swedish Innovation Agency, and in part by the Swedish Knowledge Foundation through the CAISR/SIDUS-AIR projects. The work of J. Fierrez was supported by the Spanish MINECO/FEDER through the CogniMetrics Project under Grant TEC2015-70627-R. The authors acknowledge the Halmstad University Library for its support with the open access fee

    3D Human Face Reconstruction and 2D Appearance Synthesis

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    3D human face reconstruction has been an extensive research for decades due to its wide applications, such as animation, recognition and 3D-driven appearance synthesis. Although commodity depth sensors are widely available in recent years, image based face reconstruction are significantly valuable as images are much easier to access and store. In this dissertation, we first propose three image-based face reconstruction approaches according to different assumption of inputs. In the first approach, face geometry is extracted from multiple key frames of a video sequence with different head poses. The camera should be calibrated under this assumption. As the first approach is limited to videos, we propose the second approach then focus on single image. This approach also improves the geometry by adding fine grains using shading cue. We proposed a novel albedo estimation and linear optimization algorithm in this approach. In the third approach, we further loose the constraint of the input image to arbitrary in the wild images. Our proposed approach can robustly reconstruct high quality model even with extreme expressions and large poses. We then explore the applicability of our face reconstructions on four interesting applications: video face beautification, generating personalized facial blendshape from image sequences, face video stylizing and video face replacement. We demonstrate great potentials of our reconstruction approaches on these real-world applications. In particular, with the recent surge of interests in VR/AR, it is increasingly common to see people wearing head-mounted displays. However, the large occlusion on face is a big obstacle for people to communicate in a face-to-face manner. Our another application is that we explore hardware/software solutions for synthesizing the face image with presence of HMDs. We design two setups (experimental and mobile) which integrate two near IR cameras and one color camera to solve this problem. With our algorithm and prototype, we can achieve photo-realistic results. We further propose a deep neutral network to solve the HMD removal problem considering it as a face inpainting problem. This approach doesn\u27t need special hardware and run in real-time with satisfying results

    2D and 3D computer vision analysis of gaze, gender and age

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    Human-Computer Interaction (HCI) has been an active research area for over four decades. Research studies and commercial designs in this area have been largely facilitated by the visual modality which brings diversified functionality and improved usability to HCI interfaces by employing various computer vision techniques. This thesis explores a number of facial cues, such as gender, age and gaze, by performing 2D and 3D based computer vision analysis. The ultimate aim is to create a natural HCI strategy that can fulfil user expectations, augment user satisfaction and enrich user experience by understanding user characteristics and behaviours. To this end, salient features have been extracted and analysed from 2D and 3D face representations; 3D reconstruction algorithms and their compatible real-world imaging systems have been investigated; case study HCI systems have been designed to demonstrate the reliability, robustness, and applicability of the proposed method.More specifically, an unsupervised approach has been proposed to localise eye centres in images and videos accurately and efficiently. This is achieved by utilisation of two types of geometric features and eye models, complemented by an iris radius constraint and a selective oriented gradient filter specifically tailored to this modular scheme. This approach resolves challenges such as interfering facial edges, undesirable illumination conditions, head poses, and the presence of facial accessories and makeup. Tested on 3 publicly available databases (the BioID database, the GI4E database and the extended Yale Face Database b), and a self-collected database, this method outperforms all the methods in comparison and thus proves to be highly accurate and robust. Based on this approach, a gaze gesture recognition algorithm has been designed to increase the interactivity of HCI systems by encoding eye saccades into a communication channel similar to the role of hand gestures. As well as analysing eye/gaze data that represent user behaviours and reveal user intentions, this thesis also investigates the automatic recognition of user demographics such as gender and age. The Fisher Vector encoding algorithm is employed to construct visual vocabularies as salient features for gender and age classification. Algorithm evaluations on three publicly available databases (the FERET database, the LFW database and the FRCVv2 database) demonstrate the superior performance of the proposed method in both laboratory and unconstrained environments. In order to achieve enhanced robustness, a two-source photometric stereo method has been introduced to recover surface normals such that more invariant 3D facia features become available that can further boost classification accuracy and robustness. A 2D+3D imaging system has been designed for construction of a self-collected dataset including 2D and 3D facial data. Experiments show that utilisation of 3D facial features can increase gender classification rate by up to 6% (based on the self-collected dataset), and can increase age classification rate by up to 12% (based on the Photoface database). Finally, two case study HCI systems, a gaze gesture based map browser and a directed advertising billboard, have been designed by adopting all the proposed algorithms as well as the fully compatible imaging system. Benefits from the proposed algorithms naturally ensure that the case study systems can possess high robustness to head pose variation and illumination variation; and can achieve excellent real-time performance. Overall, the proposed HCI strategy enabled by reliably recognised facial cues can serve to spawn a wide array of innovative systems and to bring HCI to a more natural and intelligent state

    Gender and gaze gesture recognition for human-computer interaction

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    © 2016 Elsevier Inc. The identification of visual cues in facial images has been widely explored in the broad area of computer vision. However theoretical analyses are often not transformed into widespread assistive Human-Computer Interaction (HCI) systems, due to factors such as inconsistent robustness, low efficiency, large computational expense or strong dependence on complex hardware. We present a novel gender recognition algorithm, a modular eye centre localisation approach and a gaze gesture recognition method, aiming to escalate the intelligence, adaptability and interactivity of HCI systems by combining demographic data (gender) and behavioural data (gaze) to enable development of a range of real-world assistive-technology applications. The gender recognition algorithm utilises Fisher Vectors as facial features which are encoded from low-level local features in facial images. We experimented with four types of low-level features: greyscale values, Local Binary Patterns (LBP), LBP histograms and Scale Invariant Feature Transform (SIFT). The corresponding Fisher Vectors were classified using a linear Support Vector Machine. The algorithm has been tested on the FERET database, the LFW database and the FRGCv2 database, yielding 97.7%, 92.5% and 96.7% accuracy respectively. The eye centre localisation algorithm has a modular approach, following a coarse-to-fine, global-to-regional scheme and utilising isophote and gradient features. A Selective Oriented Gradient filter has been specifically designed to detect and remove strong gradients from eyebrows, eye corners and self-shadows (which sabotage most eye centre localisation methods). The trajectories of the eye centres are then defined as gaze gestures for active HCI. The eye centre localisation algorithm has been compared with 10 other state-of-the-art algorithms with similar functionality and has outperformed them in terms of accuracy while maintaining excellent real-time performance. The above methods have been employed for development of a data recovery system that can be employed for implementation of advanced assistive technology tools. The high accuracy, reliability and real-time performance achieved for attention monitoring, gaze gesture control and recovery of demographic data, can enable the advanced human-robot interaction that is needed for developing systems that can provide assistance with everyday actions, thereby improving the quality of life for the elderly and/or disabled

    Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey

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    The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective.Comment: 29 pages, submitted to ACM computing survey journa

    INSPIRE Newsletter Fall 2018

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    https://scholarsmine.mst.edu/inspire-newsletters/1003/thumbnail.jp

    Vedel-objektiiv abil salvestatud kaugseire piltide analüüs kasutades super-resolutsiooni meetodeid

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    Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesolevas doktoritöös uuriti nii riist- kui ka tarkvaralisi lahendusi piltide töötlemiseks. Riist¬varalise poole pealt pakuti lahenduseks uudset vedelläätse, milles on dielekt¬rilisest elastomeerist kihilise täituriga membraan otse optilisel teljel. Doktoritöö käigus arendati välja kaks prototüüpi kahe erineva dielektrilisest elastomeerist ki¬hilise täituriga, mille aktiivne ala oli ühel juhul 40 ja teisel 20 mm. Läätse töö vas¬tas elastomeeri deformatsiooni mehaanikale ja suhtelistele muutustele fookuskau¬guses. Muutuste demonstreerimiseks meniskis ja läätse fookuskauguse mõõtmiseks kasutati laserkiirt. Katseandmetest selgub, et muutuste tekitamiseks on vajalik pinge vahemikus 50 kuni 750 volti. Tarkvaralise poole pealt pakuti uut satelliitpiltide parandamise süsteemi. Paku¬tud süsteem jagas mürase sisendpildi DT-CWT laineteisenduse abil mitmeteks sagedusalamribadeks. Pärast müra eemaldamist LA-BSF funktsiooni abil suu¬rendati pildi resolutsiooni DWT-ga ja kõrgsagedusliku alamriba piltide interpo¬leerimisega. Interpoleerimise faktor algsele pildile oli pool sellest, mida kasutati kõrgsagedusliku alamriba piltide interpoleerimisel ning superresolutsiooniga pilt rekonst¬rueeriti IDWT abil. Käesolevas doktoritöös pakuti tarkvaraliseks lahenduseks uudset sõnastiku baasil töötavat super-resolutsiooni (SR) meetodit, milles luuakse paarid suure resolutsiooniga (HR) ja madala resolut-siooniga (LR) piltidest. Kõigepealt jagati vastava sõnastiku loomiseks HR ja LR paarid omakorda osadeks. Esialgse HR kujutise saamiseks LR sisendpildist kombineeriti HR osi. HR osad valiti sõnastikust nii, et neile vastavad LR osad oleksid võimalikult lähedased sisendiks olevale LR pil¬dile. Iga valitud HR osa heledust korrigeeriti, et vähendada kõrvuti asuvate osade heleduse erine¬vusi superresolutsiooniga pildil. Plokkide efekti vähendamiseks ar¬vutati saadud SR pildi keskmine ning bikuupinterpolatsiooni pilt. Lisaks pakuti käesolevas doktoritöös välja kernelid, mille tulemusel on võimalik saadud SR pilte teravamaks muuta. Pakutud kernelite tõhususe tõestamiseks kasutati [83] ja [50] poolt pakutud resolutsiooni parandamise meetodeid. Superreso¬lutsiooniga pilt saadi iga kerneli tehtud HR pildi kombineerimise teel alpha blen¬dingu meetodit kasutades. Pakutud meetodeid ja kerneleid võrreldi erinevate tavaliste ja kaasaegsete meetoditega. Kvantita-tiivsetest katseandmetest ja saadud piltide kvaliteedi visuaal¬sest hindamisest selgus, et pakutud meetodid on tavaliste kaasaegsete meetoditega võrreldes paremad.In this thesis, a study of both hardware and software solutions for image enhance¬ment has been done. On the hardware side, a new liquid lens design with a DESA membrane located directly in the optical path has been demonstrated. Two pro¬totypes with two different DESA, which have a 40 and 20 mm active area in diameter, were developed. The lens performance was consistent with the mechan¬ics of elastomer deformation and relative focal length changes. A laser beam was used to show the change in the meniscus and to measure the focal length of the lens. The experimental results demonstrate that voltage in the range of 50 to 750 V is required to create change in the meniscus. On the software side, a new satellite image enhancement system was proposed. The proposed technique decomposed the noisy input image into various frequency subbands by using DT-CWT. After removing the noise by applying the LA-BSF technique, its resolution was enhanced by employing DWT and interpolating the high-frequency subband images. An original image was interpolated with half of the interpolation factor used for interpolating the high-frequency subband images, and the super-resolved image was reconstructed by using IDWT. A novel single-image SR method based on a generating dictionary from pairs of HR and their corresponding LR images was proposed. Firstly, HR and LR pairs were divided into patches in order to make HR and LR dictionaries respectively. The initial HR representation of an input LR image was calculated by combining the HR patches. These HR patches are chosen from the HR dictionary corre-sponding to the LR patches that have the closest distance to the patches of the in¬put LR image. Each selected HR patch was processed further by passing through an illumination enhancement processing order to reduce the noticeable change of illumination between neighbor patches in the super-resolved image. In order to reduce the blocking effect, the average of the obtained SR image and the bicubic interpolated image was calculated. The new kernels for sampling have also been proposed. The kernels can improve the SR by resulting in a sharper image. In order to demonstrate the effectiveness of the proposed kernels, the techniques from [83] and [50] for resolution enhance¬ment were adopted. The super-resolved image was achieved by combining the HR images produced by each of the proposed kernels using the alpha blending tech-nique. The proposed techniques and kernels are compared with various conventional and state-of-the-art techniques, and the quantitative test results and visual results on the final image quality show the superiority of the proposed techniques and ker¬nels over conventional and state-of-art technique

    Advanced Biometrics with Deep Learning

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    Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others
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