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.

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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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.

Biometric identification systems have several distinct advantages over other authentication technologies, such as passwords, in reliably recognising individuals. Iris based recognition is one such biometric recognition system. Unlike other biometrics such as fingerprints or face images, the distinct aspect of the iris comes from its randomly distributed features. The patterns of these randomly distributed features on the iris have been proved to be fixed in a person's lifetime, and are stable over time for healthy eyes except for the distortions caused by the constriction and dilation of the pupil. The distortion of the iris pattern caused by pupillary activity, which is mainly due changes in ambient lighting conditions, can be significant. One important question that arises from this is: How closely do two different iris images of the same person, taken at different times using different cameras, in different environments, and under different lighting conditions, agree with each other? It is also problematic for iris recognition systems to correctly identify a person when his/her pupil size is very different from the person's iris images, used at the time of constructing the system's data-base. To date, researchers in the field of iris recognition have made attempts to address this problem, with varying degrees of success. However, there is still a need to conduct in-depth investigations into this matter in order to arrive at more reliable solutions. It is therefore necessary to study the behaviour of iris surface deformation caused by the change of lighting conditions. In this thesis, a study of the physiological behaviour of pupil size variation under different normal indoor lighting conditions (100 lux ~ 1,200 lux) and brightness levels is presented. The thesis also presents the results of applying Elastic Graph Matching (EGM) tracking techniques to study the mechanisms of iris surface deformation. A study of the pupil size variation under different normal indoor lighting conditions was conducted. The study showed that the behaviour of the pupil size can be significantly different from one person to another under the same lighting conditions. There was no evidence from this study to show that the exact pupil sizes of an individual can be determined at a given illumination level. However, the range of pupil sizes can be estimated for a range of specific lighting conditions. The range of average pupil sizes under normal indoor lighting found was between 3 mm and 4 mm. One of the advantages of using EGM for iris surface deformation tracking is that it incorporates the benefit of the use of Gabor wavelets to encode the iris features for tracking. The tracking results showed that the radial stretch of the iris surface is nonlinear. However, the amount of extension of iris surface at any point on the iris during the stretch is approximately linear. The analyses of the tracking results also showed that the behaviour of iris surface deformation is different from one person to another. This implies that a generalised iris surface deformation model cannot be established for personal identification. However, a deformation model can be established for every individual based on their analysis result, which can be useful for personal verification using the iris. Therefore, analysis of the tracking results of each individual was used to model iris surface deformations for that individual. The model was able to estimate the movement of a point on the iris surface at a particular pupil size. This makes it possible to estimate and construct the 2D deformed iris image of a desired pupil size from a given iris image of another different pupil size. The estimated deformed iris images were compared with their actual images for similarity, using an intensitybased (zero mean normalised cross-correlation). The result shows that 86% of the comparisons have over 65% similarity between the estimated and actual iris image. Preliminary tests of the estimated deformed iris images using an open-source iris recognition algorithm have showed an improved personal verification performance. The studies presented in this thesis were conducted using a very small sample of iris images and therefore should not be generalised, before further investigations are conducted.

Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution. As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions. This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations. A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.

Made available in DSpace on 2015-07-22T20:49:36Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-01-27 / Objetivo: Avaliar os métodos biométricos do olho humano em pacientes portadores de catarata e com indicação de implante de lente intra-ocular. Métodos: Oitenta e quatro pacientes com média de idade de 58,82 ± 10,03 anos foram submetidos às biometrias óptica e ultra-sônica (imersão e contato) em ambos os olhos (n = 168). Os dados do comprimento axial, profundidade da câmara anterior, espessura do cristalino, profundidade da câmara vítrea e curvatura da córnea foram analisados. Foi avaliada a diferença das médias de cada método biométrico usado e a concordância foi investigada pela análise gráfica intra-individual de Bland-Altman de um método em relação ao outro. Resultados: Não houve diferença estatisticamente significante para as médias do comprimento axial (P=0,245), profundidade da câmara anterior (P=0,073), espessura do cristalino (P=0,8794) e profundidade da câmara vítrea (P=0,7640) entre os métodos usados. A diferença das médias da curvatura da córnea entre o IOLMaster® e o ceratômetro manual OM-4® foi estatisticamente significante (P<0,0001). Apesar da boa reprodutibilidade dos resultados, os gráficos de Bland- Altman mostraram algumas medidas fora dos limites de concordância estabelecidos em todas as variáveis analisadas. Houve menor concordância das medidas ceratométricas na comparação entre o IOLMaster® e o ceratômetro OM- 4®. Conclusões: Não houve diferença das médias dos métodos biométricos usados nesse estudo, exceto a curvatura da córnea que apresentou uma diferença estatisticamente significante entre as médias apresentadas. Os métodos demonstraram boa concordância, entretanto algumas medidas se apresentaram discordantes e podem ocasionar erros no cálculo do poder dióptrico da lente intraocular. / TEDE / BV UNIFESP: Teses e dissertações

Foi desenvolvido nesta pesquisa um estudo sobre a utilização de imagens de olhos humanos em um sistema biométricos de identificação. Este trabalho apresenta os resultados obtidos na comparação de olhos humanos utilizando a técnica Scale Invariant Feature Transform (SIFT). A técnica SIFT é uma ferramenta capaz de identificar objetos, tendo como principais características: a invariância as transformações de rotação, translação, escala e oclusão do objeto dentro da imagem. Uma pesquisa sobre os principais sistemas biométricos de identificação existentes foi realizada. Para as comparações entre as imagens utilizou-se um banco de imagens de olhos humanos denominado, UBIRIS, obtendo resultados muito interessantes. / This research developed a study about the use of images of human eyes in a biometric identification system. This work presents the results of the comparison of human eyes using the technique Scale Invariant Feature Transform (SIFT). The SIFT technique is a tool capable of identify objects, with the main features: the alteration of rotation invariance, translation, scale and occlusion of the object within the picture. A search on the main systems of biometric identification was made. For the comparisons between the images we used a bank of images of human eyes called UBIRIS, getting very interesting results.

Made available in DSpace on 2014-06-11T19:33:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-01-19Bitstream added on 2014-06-13T20:25:10Z : No. of bitstreams: 1 oliveira_rv_dr_jabo.pdf: 622405 bytes, checksum: 675f6cc1469564dbb7aea9fff46bae14 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Objetivou-se com o presente trabalho avaliar os efeitos do Lactobacillus plantarum (LP), Lactobacillus buchneri (LB), Benzoato de sódio (BS) e suas associações (LPLB e LPBS) sobre a redução das perdas quantitativas e qualitativas das silagens de grãos úmidos de milho (SGUM) e o controle da população de leveduras e fungos durante a exposição aeróbia das mesmas; os efeitos sobre a digestibilidade e o desempenho de cabritos alimentados com essas silagens; assim como a utilização da ultrassonografia e medidas biométricas como método indireto na estimativa das características da carcaça. Para tanto, foram realizados dois experimentos: no experimento 1 foram utilizados silos experimentais, nos quais foram determinadas as perdas de matéria seca (MS) por gases (PG), a recuperação de MS, teores de MS, nitrogênio amoniacal, valores de pH ocorrido durante a fermentação e estabilidade aeróbia (EA), assim como contagem de fungos e leveduras durante a EA das silagens. Na avaliação do processo fermentativo, utilizou-se o delineamento inteiramente casualizado (DIC), em arranjo fatorial 6 X 6 (6 silagens e 6 tempos) e na fase de exposição aeróbia utilizou-se um DIC em parcelas subdivididas, sendo as parcelas representadas pelas silagens experimentais e as sub-parcelas pelos tempos de exposição (0, 4, 8 e 12 dias). O experimento 2 consistiu de dois ensaios de digestibilidade e um de desempenho utilizando-se 24 cabritos (16 ¾ Boer e ¼ Saanen e 8 Saanen puros) machos, castrados e confinados por 84 dias. Foram determinadas as medidas biométricas, condição corporal e as mensurações através do ultrassom no animal vivo, assim como as medidas da carcaça após o abate e resfriamento por 24 h. Utilizou-se o DIC em parcelas subdivididas, tendo na parcela um fatorial 4 X 2 (4 rações e 2 grupos genéticos) e na subparcela os ensaios. As rações foram compostas por feno de Tifton... / The objective of this study was to evaluate the effects of Lactobacillus plantarum (LP), Lactobacillus buchneri (LB), Sodium benzoate (SB) and their associations (LPLB and LPBS) on the reduction of quantitative and qualitative losses of high moisture corn silages (HMCS) and the control of yeast and fungi populations during fermentation and aerobic exposition of the same, the effects on digestibility and performance of goats fed with these silages, as well as the use of ultrasound and biometric measures as indirect method to estimate the carcass characteristics. For this, two experiments were conducted: in the first experiment were used experimental silos, in which were determined the losses of dry matter for gases (GL), the recovery of dry matter (DM), levels of DM, ammonia nitrogen, pH values occurred during fermentation and aerobic stability (AS), as well as fungi and yeast counts during the AS of the silage. In evaluating of the fermentation process, was used the completely randomized design (CRD) in a factorial arrangement 6 X 6 (6 silages and 6 times) and in the aerobic exposition phase was used a split-plot in CRD, and the plots were represented by experimental silages and the sub-plots by time of exposition (0, 4, 8 and 12 days). The second experiment consisted of two digestibility trials and one about performance of 24 goats (16 ¾ Boer and ¼ Saanen and 8 Saanen blood pure) males, castrated and feedlot for 84 days. Biometric and ultrasound measures in the living animal were determined, as well as carcass measures after slaughtered and chilling for 24 hours. The CRD was used in split-plot arrangement, using in the plots a factorial 4 x 2 arrangement (4 rations and 2 genetic groups) and the sub-plots the trials. The rations were composed by Tifton 85 hay (53.3%), soybean meal (12.15%), urea (0.25%) and mineral mixture (2%), being added... (Complete abstract click electronic access below)