Relevant bibliographies by topics / Liveness detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Liveness detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

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Journal articles on the topic "Liveness detection"

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Marcialis, Gian Luca, and Fabio Roli. "Liveness detection competition 2009." Biometric Technology Today 17, no. 3 (March 2009): 7–9. http://dx.doi.org/10.1016/s0969-4765(09)70038-4.

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Xin, Yang, Yi Liu, Zhi Liu, Xuemei Zhu, Lingshuang Kong, Dongmei Wei, Wei Jiang, and Jun Chang. "A survey of liveness detection methods for face biometric systems." Sensor Review 37, no. 3 (June 19, 2017): 346–56. http://dx.doi.org/10.1108/sr-08-2015-0136.

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Purpose Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches. Design/methodology/approach This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures. Originality/value This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.
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Rani, Rajneesh, and Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach." International Journal of Intelligent Information Technologies 17, no. 1 (January 2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.

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In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).
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Drahansky, Martin, Michal Dolezel, Jan Vana, Eva Brezinova, Jaegeol Yim, and Kyubark Shim. "New Optical Methods for Liveness Detection on Fingers." BioMed Research International 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/197925.

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This paper is devoted to new optical methods, which are supposed to be used for liveness detection on fingers. First we describe the basics about fake finger use in fingerprint recognition process and the possibilities of liveness detection. Then we continue with introducing three new liveness detection methods, which we developed and tested in the scope of our research activities—the first one is based on measurement of the pulse, the second one on variations of optical characteristics caused by pressure change, and the last one is based on reaction of skin to illumination with different wavelengths. The last part deals with the influence of skin diseases on fingerprint recognition, especially on liveness detection.
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Wu, Lifang, Yaowen Xu, Meng Jian, Xiao Xu, and Wei Qi. "Face liveness detection scheme with static and dynamic features." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (March 2018): 1840001. http://dx.doi.org/10.1142/s0219691318400015.

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Face liveness detection is a significant research topic in face-based online authentication. The current face liveness detection approaches utilize either static or dynamic features, but not both. In fact, the dynamic and static features have different advantages in face liveness detection. In this paper, we propose a scheme combining dynamic and static features to capture merits of them for face liveness detection. First, the dynamic maps are captured from the inter-frame motion in the video, which investigates motion information of the face in the video. Then, with a Convolutional Neural Network (CNN), the dynamic and static features are extracted from the dynamic maps and the frame images, respectively. Next, in CNN, the fully connected layers containing the dynamic and static features are concatenated to form a fused feature. Finally, the fused features are used to train a binary Support Vector Machine (SVM) classifier, which classifies the frames into two categories, i.e. frame with real or fake face. Experimental results and the corresponding analysis demonstrate that the proposed scheme is capable of discovering face liveness by fusing dynamic and static features and it outperforms the current state-of-the-art face liveness detection approaches.
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Raheem, Enas A., Sharifah Mumtazah Syed Ahmad, and Wan Azizun Wan Adnan. "Insight on face liveness detection: A systematic literature review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 5865. http://dx.doi.org/10.11591/ijece.v9i6.pp5865-5175.

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<p>To review researcher’s attempts in response to the problem of spoofing and liveness detection, mapping the research overview from the literature survey into a suitable taxonomy, exploring the basic properties of the field, motivation of using liveness detection methods in face recognition, and Problems that may restrain the advantages. We presented a subjected search on face recognition with liveness detection and its synonyms in four main databases: Web of science, Science Direct, Scopus and IEEE Xplore. We believe that these databases are widely inclusive enough to cover the literature.<em> </em>The final number of articles considered is 65 articles. 4 of them where review and survey articles that described a general overview about liveness detection and anti-spoofing methods. Since 2012, and despite of leaving some areas unestablished and needs more attention, researchers tried to keep track of liveness detection in several ways. No matter what their category is, articles concentrated on challenges that faces the full utility of anti-spoofing methods and recommended some solutions to overcome these challenges. In this paper, different types of liveness detection and face anti-spoofing techniques are investigated to keep researchers updated with what is being developed in this field.</p>
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Khairnar, Smita, Shilpa Gite, Ketan Kotecha, and Sudeep D. Thepade. "Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions." Big Data and Cognitive Computing 7, no. 1 (February 17, 2023): 37. http://dx.doi.org/10.3390/bdcc7010037.

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Biometrics has been evolving as an exciting yet challenging area in the last decade. Though face recognition is one of the most promising biometrics techniques, it is vulnerable to spoofing threats. Many researchers focus on face liveness detection to protect biometric authentication systems from spoofing attacks with printed photos, video replays, etc. As a result, it is critical to investigate the current research concerning face liveness detection, to address whether recent advancements can give solutions to mitigate the rising challenges. This research performed a systematic review using the PRISMA approach by exploring the most relevant electronic databases. The article selection process follows preset inclusion and exclusion criteria. The conceptual analysis examines the data retrieved from the selected papers. To the author, this is one of the foremost systematic literature reviews dedicated to face-liveness detection that evaluates existing academic material published in the last decade. The research discusses face spoofing attacks, various feature extraction strategies, and Artificial Intelligence approaches in face liveness detection. Artificial intelligence-based methods, including Machine Learning and Deep Learning algorithms used for face liveness detection, have been discussed in the research. New research areas such as Explainable Artificial Intelligence, Federated Learning, Transfer learning, and Meta-Learning in face liveness detection, are also considered. A list of datasets, evaluation metrics, challenges, and future directions are discussed. Despite the recent and substantial achievements in this field, the challenges make the research in face liveness detection fascinating.
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Singh, Manminder, and A. S. Arora. "A Novel Face Liveness Detection Algorithm with Multiple Liveness Indicators." Wireless Personal Communications 100, no. 4 (April 6, 2018): 1677–87. http://dx.doi.org/10.1007/s11277-018-5661-1.

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Moon, Y. S., J. S. Chen, K. C. Chan, K. So, and K. C. Woo. "Wavelet based fingerprint liveness detection." Electronics Letters 41, no. 20 (2005): 1112. http://dx.doi.org/10.1049/el:20052577.

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Kim, Sooyeon, Yuseok Ban, and Sangyoun Lee. "Face Liveness Detection Using Defocus." Sensors 15, no. 1 (January 14, 2015): 1537–63. http://dx.doi.org/10.3390/s150101537.

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Dissertations / Theses on the topic "Liveness detection"

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Sandström, Marie. "Liveness Detection in Fingerprint Recognition Systems." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2397.

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Biometrics deals with identifying individuals with help of their biological data. Fingerprint scanning is the most common method of the biometric methods available today. The security of fingerprint scanners has however been questioned and previous studies have shown that fingerprint scanners can be fooled with artificial fingerprints, i.e. copies of real fingerprints. The fingerprint recognition systems are evolving and this study will discuss the situation of today.

Two approaches have been used to find out how good fingerprint recognition systems are in distinguishing between live fingers and artificial clones. The first approach is a literature study, while the second consists of experiments.

A literature study of liveness detection in fingerprint recognition systems has been performed. A description of different liveness detection methods is presented and discussed. Methods requiring extra hardware use temperature, pulse, blood pressure, electric resistance, etc., and methods using already existent information in the system use skin deformation, pores, perspiration, etc.

The experiments focus on making artificial fingerprints in gelatin from a latent fingerprint. Nine different systems were tested at the CeBIT trade fair in Germany and all were deceived. Three other different systems were put up against more extensive tests with three different subjects. All systems werecircumvented with all subjects'artificial fingerprints, but with varying results. The results are analyzed and discussed, partly with help of the A/R value defined in this report.

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Ali, Asad. "Biometric liveness detection using gaze information." Thesis, University of Kent, 2015. https://kar.kent.ac.uk/50524/.

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This thesis is concerned with liveness detection for biometric systems and in particular for face recognition systems. Biometric systems are well studied and have the potential to provide satisfactory solutions for a variety of applications. However, presentation attacks (spoofng), where an attempt is made at subverting them system by making a deliberate presentation at the sensor is a serious challenge to their use in unattended applications. Liveness detection techniques can help with protecting biometric systems from attacks made through the presentation of artefacts and recordings at the sensor. In this work novel techniques for liveness detection are presented using gaze information. The notion of natural gaze stability is introduced and used to develop a number of novel features that rely on directing the gaze of the user and establishing its behaviour. These features are then used to develop systems for detecting spoofng attempts. The attack scenarios considered in this work include the use of hand held photos and photo masks as well as video reply to subvert the system. The proposed features and systems based on them were evaluated extensively using data captured from genuine and fake attempts. The results of the evaluations indicate that gaze-based features can be used to discriminate between genuine and imposter. Combining features through feature selection and score fusion substantially improved the performance of the proposed features.
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GHIANI, LUCA. "Textural features for fingerprint liveness detection." Doctoral thesis, Università degli Studi di Cagliari, 2015. http://hdl.handle.net/11584/266594.

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The main topic ofmy research during these three years concerned biometrics and in particular the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints. Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords. Several videos posted on YouTube show how to violate these devices by using fake fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a threat to the existing fingerprint recognition systems. Despite the fact that many algorithms have been proposed so far, none of them showed the ability to clearly discriminate between real and fake fingertips. In my work, after a study of the state-of-the-art I paid a special attention on the so called textural algorithms. I first used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms in the FLD field. In the last two years I worked especially on what we called the “user specific” problem. In the extracted features we noticed the presence of characteristic related not only to the liveness but also to the different users. We have been able to improve the obtained results identifying and removing, at least partially, this user specific characteristic. Since 2009 the Department of Electrical and Electronic Engineering of the University of Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity have organized the Fingerprint Liveness Detection Competition (LivDet). I have been involved in the organization of both second and third editions of the Fingerprint Liveness Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets.
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Omar, Luma Qassam Abedalqader. "Face liveness detection under processed image attacks." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12812/.

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Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques.
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Dohnálek, Tomáš. "Liveness Detection on Fingers Using Vein Pattern." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234901.

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Tato práce se zabývá rozšířením snímače otisků prstů Touchless Biometric Systems 3D-Enroll o jednotku detekce živosti prstu na základě žil. Bylo navrhnuto a zkonstruováno hardwarové řešení s využitím infračervených diod. Navržené softwarové řešení pracuje ve dvou různých režimech: detekce živosti na základě texturních příznaků a verifikace uživatelů na základě porovnávání žilních vzorů. Datový soubor obsahující přes 1100 snímků jak živých prstů tak jejich falsifikátů vznikl jako součást této práce a výkonnost obou zmíněných režimů byla vyhodnocena na tomto datovém souboru. Na závěr byly navrhnuty materiály vhodné k výrobě falsifikátů otisků prstů umožňující oklamání detekce živosti pomocí žilních vzorů.
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Crihalmeanu, Musat C. "Adding liveness detection to the hand geometry scanner." Morgantown, W. Va. : [West Virginia University Libraries], 2003. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3220.

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Thesis (M.S.)--West Virginia University, 2003
Title from document title page. Document formatted into pages; contains viii, 96 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 72-74).
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Memon, Shahzad Ahmed. "Novel active sweat pores based liveness detection techniques for fingerprint biometrics." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7060.

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Liveness detection in automatic fingerprint identification systems (AFIS) is an issue which still prevents its use in many unsupervised security applications. In the last decade, various hardware and software solutions for the detection of liveness from fingerprints have been proposed by academic research groups. However, the proposed methods have not yet been practically implemented with existing AFIS. A large amount of research is needed before commercial AFIS can be implemented. In this research, novel active pore based liveness detection methods were proposed for AFIS. These novel methods are based on the detection of active pores on fingertip ridges, and the measurement of ionic activity in the sweat fluid that appears at the openings of active pores. The literature is critically reviewed in terms of liveness detection issues. Existing fingerprint technology, and hardware and software solutions proposed for liveness detection are also examined. A comparative study has been completed on the commercially and specifically collected fingerprint databases, and it was concluded that images in these datasets do not contained any visible evidence of liveness. They were used to test various algorithms developed for liveness detection; however, to implement proper liveness detection in fingerprint systems a new database with fine details of fingertips is needed. Therefore a new high resolution Brunel Fingerprint Biometric Database (B-FBDB) was captured and collected for this novel liveness detection research. The first proposed novel liveness detection method is a High Pass Correlation Filtering Algorithm (HCFA). This image processing algorithm has been developed in Matlab and tested on B-FBDB dataset images. The results of the HCFA algorithm have proved the idea behind the research, as they successfully demonstrated the clear possibility of liveness detection by active pore detection from high resolution images. The second novel liveness detection method is based on the experimental evidence. This method explains liveness detection by measuring the ionic activities above the sample of ionic sweat fluid. A Micro Needle Electrode (MNE) based setup was used in this experiment to measure the ionic activities. In results, 5.9 pC to 6.5 pC charges were detected with ten NME positions (50μm to 360 μm) above the surface of ionic sweat fluid. These measurements are also a proof of liveness from active fingertip pores, and this technique can be used in the future to implement liveness detection solutions. The interaction of NME and ionic fluid was modelled in COMSOL multiphysics, and the effect of electric field variations on NME was recorded at 5μm -360μm positions above the ionic fluid.
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Lin, B. (Bofan). "Face liveness detection by rPPG features and contextual patch-based CNN." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906052450.

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Abstract. Face anti-spoofing plays a vital role in security systems including face payment systems and face recognition systems. Previous studies showed that live faces and presentation attacks have significant differences in both remote photoplethysmography (rPPG) and texture information. We propose a generalized method exploiting both rPPG and texture features for face anti-spoofing task. First, we design multi-scale long-term statistical spectral (MS-LTSS) features with variant granularities for the representation of rPPG information. Second, a contextual patch-based convolutional neural network (CP-CNN) is used for extracting global-local and multi-level deep texture features simultaneously. Finally, weight summation strategy is employed for decision level fusion of the two types of features, which allow the proposed system to be generalized for detecting not only print attack and replay attack, but also mask attack. Comprehensive experiments were conducted on five databases, namely 3DMAD, HKBU-Mars V1, MSU-MFSD, CASIA-FASD, and OULU-NPU, to show the superior results of the proposed method compared with state-of-the-art methods.Tiivistelmä. Kasvojen anti-spoofingilla on keskeinen rooli turvajärjestelmissä, mukaan lukien kasvojen maksujärjestelmät ja kasvojentunnistusjärjestelmät. Aiemmat tutkimukset osoittivat, että elävillä kasvoilla ja esityshyökkäyksillä on merkittäviä eroja sekä etävalopölymografiassa (rPPG) että tekstuuri-informaatiossa, ehdotamme yleistettyä menetelmää, jossa hyödynnetään sekä rPPG: tä että tekstuuriominaisuuksia kasvojen anti-spoofing -tehtävässä. Ensinnäkin rPPG-informaation esittämiseksi on suunniteltu monivaiheisia pitkän aikavälin tilastollisia spektrisiä (MS-LTSS) ominaisuuksia, joissa on muunneltavissa olevat granulariteetit. Toiseksi, kontekstuaalista patch-pohjaista konvoluutioverkkoa (CP-CNN) käytetään globaalin paikallisen ja monitasoisen syvään tekstuuriominaisuuksiin samanaikaisesti. Lopuksi, painoarvostusstrategiaa käytetään päätöksentekotason fuusioon, joka auttaa yleistämään menetelmää paitsi hyökkäys- ja toistoiskuille, mutta myös peittää hyökkäyksen. Kattavat kokeet suoritettiin viidellä tietokannalla, nimittäin 3DMAD, HKBU-Mars V1, MSU-MFSD, CASIA-FASD ja OULU-NPU, ehdotetun menetelmän parempien tulosten osoittamiseksi verrattuna uusimpiin menetelmiin.
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Das, Abhijit. "Towards Multi-modal Sclera and Iris Biometric Recognition with Adaptive Liveness Detection." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/370828.

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Security breaches due to misidentification of an individual pose one of the greatest threats and challenges for today’s world. The use of biometrics can be quite promising in minimising this threat. Biometrics refers to the automatic authentication of individuals based on their physiological and behavioural characteristics. To date, various biometric systems have been proposed in the literature, among them biometric traits such as the face, iris, fingerprints, retina, gait, and vocal patterns are found to be distinctive to each and every person and are considered to be most reliable biometric identifiers. Regardless of the available biometrics traits, to date, no biometric system has been found to be a perfect, and which can be applied universally in a way that is robust/adaptive to change in different environmental conditions. Multimodal biometric systems were proposed in the late 1990’s to extend the range of biometric applicability. In a multimodal biometric system, two or more biometric identifiers are fused by an information fusion technique, thereby providing robustness for changing in a greater range of environmental conditions and enhancing other properties that an ideal biometric system should possess. Another important property that a biometric system should possess is a capability to distinguish between real and fake data. Although both the robustness of the system and capability to distinguish between a real and fake data should be incorporated into a single system, there is a trade-off. Therefore, due to the aforementioned research problems, this thesis addresses advancements in multimodal ocular biometrics using iris and sclera and also investigates the trade-off between robustness/adaptability and anti-spoofing/liveness detection (which is one method to distinguish between real and fake data). Biometrics traits that allow personal identification, eye traits offer a good choice of biometrics, as the eye offers a wide range of unique characteristics. The two common eye biometric identifiers that can be found in the literature are the iris and retina. Two more biometrics that are becoming popular nowadays are the sclera and the peri-ocular. The iris biometric is believed to be the most reliable eye biometric and that is why various commercial products based on this biometric are available; but the iris biometric used in an unconstrained scenario is still an open research area. The performance of iris biometrics with changes in the gaze angle of the eye can be affected highly. Therefore, due to this restriction, high user cooperation is required by persons with squinty eyes to get successfully identified in an iris biometric system. Identifying individuals with darker irises is another big challenge in iris recognition in the visible spectrum. To mitigate this problem, multi-modal eye biometrics was proposed by combining iris and sclera traits in the visible spectrum. However, in order to establish the concept of multimodal eye biometrics using the iris and sclera, it is first necessary to assess if sufficient discriminatory information can be gained from the sclera, further assessment in regards to its combination with the iris pattern and adaptiveness of the traits with respect to changes in environmental conditions, population, the data acquisition technique and time span. Multimodal biometrics using sclera and iris have not been extensively studied and little is known regarding their usefulness. So, the state-of-the-art related to it is not sufficiently mature and still in its infancy. This thesis concentrates on designing an image processing and pattern recognition module for evaluating the potential of the scleral biometric with regards to biometric accuracy. Thus, research is also carried out investigate usefulness of the sclera trait in combination with the iris pattern. Various, pre-processing techniques, segmentation, feature extraction, information fusion and classification techniques are employed to push the border of this multimodal biometrics. The latter half of the thesis concentrates on bridging the anti-spoofing technique liveliness with adaptiveness of biometrics. Traditional biometric systems are not equipped to distinguish between fake and real data that has been scanned in front of the sensors. As a result, they adhere to forgery attacks by intruders who can take the privilege of a genuine user. With the rising demand of involuntary or unmanned biometric systems in border security, flight checking, and other restricted zones, the incorporation of the automatic detection of forgery attacks is becoming very obvious. Adaptability of the system with respect to the change in the trait is another important aspect that this biometric system should be enriched with. As mentioned previously both the forgery detection method (termed as liveness detection in the literature of biometrics) and adaptability of the trait is necessary for a trusted involuntary biometric system, but initial studies in the literature exhibit it as a trade-off. Therefore to fulfil the gap, this thesis aimed to propose a new framework for software-based liveness detection, which is also associated to the adaptability of the trait. To fulfil the above-highlighted aim in the proposed framework, intra-class level (i.e. user level) liveness detection is introduced employing image quality-based features. Furthermore, to incorporate the adaptability of the trait, online learning-based classifiers are used. Initial investigation and experimental results solicit the use of the proposed framework for trusted involuntary biometric systems. Two new multi-angle eye datasets were developed and published as a part of the current research. The thesis also consists of contributions to other fields of pattern recognition such as wrist vein biometrics, multiscript signature verification and script identification.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Matthew, Peter. "Novel approaches to biometric security with an emphasis on liveness and coercion detection." Thesis, Edge Hill University, 2016. http://repository.edgehill.ac.uk/7129/.

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Chapter One contains the introduction, sets the context and aims of the research and highlights the original contribution to knowledge along with publications gained from this research. Chapter Two will cover the methodological choice, which is grounded theory, as well as highlighting other potential methodologies that while are viable have not been used. Chapter Three will identify the background to biometric security while identifying some of the key areas that are currently lacking such as an appropriate way of measuring liveness detection techniques as well as the entire coercion detection sub-discipline. Chapter Four discusses the development of a new taxonomy that will classify liveness detection while moving away from the current ordinal measurement system used within the research area. Analysis of these liveness classifiers will then follow leading into the adaptation of the taxonomy of coercion detection techniques. Further development of these new techniques will follow, identifying metrics for coercion detection and an analysis of the proposed classifiers. After the taxonomy development Chapter Five analyses coercion and liveness techniques by 1.6 Published Work 7 applying the taxonomy across a selection of liveness and coercion techniques. This will then be followed by the development of an algorithm to denote the level of security an individual technique has achieved. Explanation of the algorithm development, components and testing will then be included. Finally Chapter Six will contain the final concluding remarks and will cover some of the areas in the future that can be looked into, alongside some focuses for article and conference submission.
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Book chapters on the topic "Liveness detection"

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Melnikov, Aleksandr, Rasim Akhunzyanov, Oleg Kudashev, and Eugene Luckyanets. "Audiovisual Liveness Detection." In Image Analysis and Processing — ICIAP 2015, 643–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23234-8_59.

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Toth, Bori. "Liveness Detection: Iris." In Encyclopedia of Biometrics, 931–38. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_179.

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Schuckers, Stephanie A. C. "Liveness Detection: Fingerprint." In Encyclopedia of Biometrics, 924–31. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_68.

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Johnson, Peter, and Stephanie Schuckers. "Fingerprint Spoofing and Liveness Detection." In Forensic Science, 373–82. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2016. http://dx.doi.org/10.1002/9783527693535.ch16.

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Batsukh, Bat-Erdene. "Liveness Detection via Facial Expressions Queue." In Advances in Intelligent Systems and Computing, 73–76. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55187-2_7.

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Shidnekoppa, Rekha A., Manjunath Kammar, and K. S. Shreedhar. "Liveness Detection Based on Eye Flicker." In Communications in Computer and Information Science, 71–80. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-9059-2_8.

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Ghiani, Luca, Paolo Denti, and Gian Luca Marcialis. "Experimental Results on Fingerprint Liveness Detection." In Articulated Motion and Deformable Objects, 210–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31567-1_21.

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Chan, Patrick P. K., and Ying Shu. "Face Liveness Detection by Brightness Difference." In Communications in Computer and Information Science, 144–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45652-1_16.

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Pala, Federico, and Bir Bhanu. "Deep Triplet Embedding Representations for Liveness Detection." In Deep Learning for Biometrics, 287–307. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61657-5_12.

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Toosi, Amirhosein, Sandro Cumani, and Andrea Bottino. "On Multiview Analysis for Fingerprint Liveness Detection." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 143–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25751-8_18.

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Conference papers on the topic "Liveness detection"

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Garud, Dhananjay, and S. S. Agrwal. "Face liveness detection." In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE, 2016. http://dx.doi.org/10.1109/icacdot.2016.7877695.

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Sundaran, Sreejit, Joycy K. Antony, and K. Vipin. "Biometrie liveness authentication detection." In 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8276098.

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Rogmann, Nils, and Maximilian Krieg. "Liveness Detection in Biometrics." In 2015 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE, 2015. http://dx.doi.org/10.1109/biosig.2015.7314611.

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Akhtar, Zahid, Christian Micheloni, and Gian Luca Foresti. "Correlation based fingerprint liveness detection." In 2015 International Conference on Biometrics (ICB). IEEE, 2015. http://dx.doi.org/10.1109/icb.2015.7139054.

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Al-Ajlan, Amani. "Survey on fingerprint liveness detection." In 2013 International Workshop on Biometrics and Forensics (IWBF 2013). IEEE, 2013. http://dx.doi.org/10.1109/iwbf.2013.6547309.

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Ali, Asad, Farzin Deravi, and Sanaul Hoque. "Liveness Detection Using Gaze Collinearity." In 2012 Third International Conference on Emerging Security Technologies (EST). IEEE, 2012. http://dx.doi.org/10.1109/est.2012.12.

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Chen, Yangyu, and Weigang Zhang. "Iris Liveness Detection: A Survey." In 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM). IEEE, 2018. http://dx.doi.org/10.1109/bigmm.2018.8499061.

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Hassan, Mehad Araby, Mohamed Nabil Mustafa, and Ayman Wahba. "Automatic liveness detection for facial images." In 2017 12th International Conference on Computer Engineering and Systems (ICCES). IEEE, 2017. http://dx.doi.org/10.1109/icces.2017.8275306.

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Kim, Sooyeon, Sunjin Yu, Kwangtaek Kim, Yuseok Ban, and Sangyoun Lee. "Face liveness detection using variable focusing." In 2013 International Conference on Biometrics (ICB). IEEE, 2013. http://dx.doi.org/10.1109/icb.2013.6613002.

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Matthew, Peter, and Mark Anderson. "Novel Categorisation Techniques for Liveness Detection." In 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies (NGMAST). IEEE, 2014. http://dx.doi.org/10.1109/ngmast.2014.51.

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