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Secrets of a smile? Your gender and perhaps your biometric identityUgail, Hassan 11 June 2018 (has links)
No / With its numerous applications, automatic facial emotion recognition has recently become a very active area of research. Yet there has been little detailed study of the dynamic components of facial expressions. This article reviews research that shows gender is encoded in the dynamics of a smile, and how it may be possible to use the dynamic components of facial expressions as a form of biometric.
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VLSI Architecture and FPGA Prototyping of a Secure Digital Camera for Biometric ApplicationAdamo, Oluwayomi Bamidele 08 1900 (has links)
This thesis presents a secure digital camera (SDC) that inserts biometric data into images found in forms of identification such as the newly proposed electronic passport. However, putting biometric data in passports makes the data vulnerable for theft, causing privacy related issues. An effective solution to combating unauthorized access such as skimming (obtaining data from the passport's owner who did not willingly submit the data) or eavesdropping (intercepting information as it moves from the chip to the reader) could be judicious use of watermarking and encryption at the source end of the biometric process in hardware like digital camera or scanners etc. To address such issues, a novel approach and its architecture in the framework of a digital camera, conceptualized as an SDC is presented. The SDC inserts biometric data into passport image with the aid of watermarking and encryption processes. The VLSI (very large scale integration) architecture of the functional units of the SDC such as watermarking and encryption unit is presented. The result of the hardware implementation of Rijndael advanced encryption standard (AES) and a discrete cosine transform (DCT) based visible and invisible watermarking algorithm is presented. The prototype chip can carry out simultaneous encryption and watermarking, which to our knowledge is the first of its kind. The encryption unit has a throughput of 500 Mbit/s and the visible and invisible watermarking unit has a max frequency of 96.31 MHz and 256 MHz respectively.
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Analysis of Fingerprint Recognition Performance on InfantsSamuel J Reiff (9183044) 29 July 2020 (has links)
<p>In
this study, any change in fingerprint performance, image quality and minutiae
count for infants in three different age groups was evaluated (0-6, 7-12, and >12
months). This was done to determine whether there is a difference in performance
between infant age groups for a fingerprint recognition system.</p>
<p>The purpose of this
research was to determine whether there is a difference in infant fingerprint
performance and image quality metrics, between three different age groups (0-6,
7-12, and >12 months old), using the same optical sensor? The data used for
this secondary analysis was collected as part of a longitudinal multimodal
infant study, using the Digital Persona U.are.U 4500. DET curves, zoo analysis,
and image quality metrics were used to evaluate performance and quality
factored by infant age group.</p><p>This
study found that there was a difference in image quality and minutiae count,
genuine and impostor match scores, and performance error rates (EER) between
the three age groups. Therefore, quality and performance were dependent on age.
While there was a difference in performance between age groups, there was
generally stability for subjects who overlapped between multiple age groups.
Difference in performance was most likely due to the difference in physical
characteristics between subjects in each age group, rather than individual
instability. The results showed that it could potentially be feasible to use
fingerprint recognition for children over the age of 12 months.</p>
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Facial and keystroke biometric recognition for computer based assessmentsAdetunji, Temitope Oluwafunmilayo 12 1900 (has links)
M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Computer based assessments have become one of the largest growing sectors in both nonacademic
and academic establishments. Successful computer based assessments require
security against impersonation and fraud and many researchers have proposed the use of
Biometric technologies to overcome this issue. Biometric technologies are defined as a
computerised method of authenticating an individual (character) based on behavioural and
physiological characteristic features. Basic biometric based computer based assessment
systems are prone to security threats in the form of fraud and impersonations. In a bid to
combat these security problems, keystroke dynamic technique and facial biometric
recognition was introduced into the computer based assessment biometric system so as to
enhance the authentication ability of the computer based assessment system. The keystroke
dynamic technique was measured using latency and pressure while the facial biometrics was
measured using principal component analysis (PCA). Experimental performance was carried
out quantitatively using MATLAB for simulation and Excel application package for data
analysis. System performance was measured using the following evaluation schemes: False
Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER) and Accuracy
(AC), for a comparison between the biometric computer based assessment system with and
without the keystroke and face recognition alongside other biometric computer based
assessment techniques proposed in the literature. Successful implementation of the proposed
technique would improve computer based assessment’s reliability, efficiency and
effectiveness and if deployed into the society would improve authentication and security
whilst reducing fraud and impersonation in our society.
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Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris ImagesYoumaran, Richard 02 February 2011 (has links)
Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.
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Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris ImagesYoumaran, Richard 02 February 2011 (has links)
Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.
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Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris ImagesYoumaran, Richard 02 February 2011 (has links)
Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.
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Návrh audio-vizuální databáze pro extrakci sekundárních biometrických a ne-biometrických znaků / Audio-visual database for soft biometric and non-biometric traits extractionHoleksa, David January 2015 (has links)
The aim of this semester project is to design a suitable database structure for the audio-visual data, parts extracted from the data corresponding to secondary biometric and non-biometric characteristics and description of these characters. The resulting database will be used for research into the identification of audio-visual content of multimedia data.
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Generování kryptografického klíče z biometrických vlastností oka / Generation of Cryptographic Key from Eye Biometric FeaturesSemerád, Lukáš January 2014 (has links)
The main topic of the thesis is creation of formulas for the amount of information entropy in biometric characteristics of iris and retina. This field of science in biometrics named above is unstudied yet, so the thesis tries to initiate research in this direction. The thesis also discusses the historical context of security and identification fields according to biometric characteristics of a human being with an overlap for potential usage in biometrics of iris and retina. The Daugman’s algorithm for converting iris image into the binary code which can be used as a cryptographic key is discussed in detail. An application implementing this conversion is also a part of the thesis.
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Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris ImagesYoumaran, Richard January 2011 (has links)
Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.
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