The use of least significant bit (LSB) and knight tour algorithm for image steganography of cover image

Steganography is one of the method to communicate in a hidden way. In another word, steganography literally means the practice of hiding messages or information within another data. Previous studies have proposed various steganography techniques using different approaches including Least Significant Bit (LSB), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). However, different approaches still have its own weaknesses. Therefore image stenography using Knight Tour Algorithm with Least Significant Bit (LSB) technique is presented. The main objective is to improve the security factor in the stego image. Basically, the proposed technique is divided into two parts which are the sender and receiver side. Then, steganalysis which is a type of attack on stenography algorithm is used to detect the secret message in the cover image by the statistical analysis of pixel values. Chi Square Statistical Attach which is one of the type of steganalysis is used to detect these near-equal Po Vs in images and bases the probability of embedding on how close to equal the even pixel values and their corresponding odd pixel values are in the test image. The Knight Tour Algorithm is applied due to the common Least Significant Bit technique that is weak in security and easily decoded by outsider

A New Fingerprint Enhancement Approach Using Image Fusion of Histogram Equalisation and Skeleton

Fingerprint classification is a technique used to assign fingerprints into five established classes namely Whorl, Left loop, Right loop, Arch and Tented Arch based on their ridge structures and singular points’ trait. Although some progresses have been made thus far to improve accuracy rates, problem arises from ambiguous fingerprints is far from over, especially in large intra-class and small inter-class variations. Poor quality images including blur, dry, wet, low-contrast, cut, scarred and smudgy, are equally challenging. As a good start of work, fingerprint image enhancement has been focused on this study. It begins with greyscale normalization, followed by histogram equalization, binarization, skeletonization and ends with image fusion, which eventually produces high quality images with clear ridge flows. 27,000 fingerprint images acquired from The National Institute of Standard and Technology (NIST) Special Database 14, which is de facto dataset for experimental in this study. With the multi-type enhancement method, the fingerprint images became clearly visible

Footscanid

Resumo: O objetivo deste trabalho é comprovar que a identificação automática de recém-nascidos através de imagens palmares e plantares em alta resolução é praticável. As características necessárias ao reconhecimento destes indivíduos são difíceis de serem obtidas nestas imagens, uma vez que as cristas possuem em média espessura entre 2,5 e 3 vezes menor do que em adultos. Além do mais, estas cristas são muito frágeis em recém-nascidos, deformando-se facilmente ao contato. Atualmente, pelo nosso conhecimento, não ia nenhum sistema biométrico ou equipamento comercialmente disponível que possa ser utilizado para o propósito de reconhecimento de recêm-nascidos. Os métodos não-automáticos atualmente utilizados para tal fim são limitados, aplicáveis somente em ambientes hospitalares e não apresentam soluções eficientes para fins de identificação neonatal, tais como: evitar trocas de bebês, roubo, tráfico e até mesmo uma futura confirmação da identidade do indivíduo. Este documento apresenta um trabalho pioneiro no campo da identificação automática de recém-nascidos, desde a coleta e processamento das imagens até os algoritmos de reconhecimento. A abordagem proposta faz uso de impressões palmares e plantares capturadas por um sensor comercialmente disponível e com resolução de 1000dpi, próxima á estimada como adequada aos propósitos desta aplicação (1500dpi). O sistema biométrico proposto apresenta-se como uma solução prática, baseando-se em métodos não-invasivos de fácil aplicação e aceitação. Experimentos realizados em 1221 impressões palmares e 1221 impressões plantares de 250 recém-nascidos da maternidade do Hospital das Clínicas da UFPR atestam a viabilidade do sistema proposto

Automatic fingerprint classification scheme using template matching with new set of singular point-based features

Fingerprint classification is a technique used to assign fingerprints into five established classes namely Whorl, Left loop, Right loop, Arch and Tented Arch based on their ridge structures and singular points’ trait. Although some progresses have been made thus far to improve accuracy rates, problem arises from ambiguous fingerprints is far from over, especially in large intra-class and small inter-class variations. Poor quality images including blur, dry, wet, low-contrast, cut, scarred and smudgy, are equally challenging. Thus, this thesis proposes a new classification technique based on template matching using fingerprint salient features as a matching tool. Basically, the methodology covers five main phases: enhancement, segmentation, orientation field estimation, singular point detection and classification. In the first phase, it begins with greyscale normalization, followed by histogram equalization, binarization, skeletonization and ends with image fusion, which eventually produces high quality images with clear ridge flows. Then, at the beginning of the second phase, the image is partitioned into 16x16 pixels blocks - for each block, local threshold is calculated using its mean, variance and coherence. This threshold is then used to extract a foreground. Later, the foreground is enhanced using a newly developed filling-in-the-gap process. As for the third phase, a new mask called Epicycloid filter is applied on the foreground to create true-angle orientation fields. They are then grouped together to form four distinct homogenous regions using a region growing technique. In the fourth phase, the homogenous areas are first converted into character-based regions. Next, a set of rules is applied on them to extract singular points. Lastly, at the classification phase, basing on singular points’ occurrence and location along to a symmetric axis, a new set of fingerprint features is created. Subsequently, a set of five templates in which each one of them represents a specific true class is generated. Finally, classification is performed by calculating a similarity between the query fingerprint image and the template images using x2 distance measure. The performance of the current method is evaluated in terms of accuracy using all 27,000 fingerprint images acquired from The National Institute of Standard and Technology (NIST) Special Database 14, which is de facto dataset for development and testing of fingerprint classification systems. The experimental results are very encouraging with accuracy rate of 93.05% that markedly outpaced the renowned researchers’ latest works

A Longitudinal Analysis on the Feasibility of Iris Recognition Performance for Infants 0-2 Years Old

The focus of this study was to longitudinally evaluate iris recognition for infants between the ages of 0 to 2 years old. Image quality metrics of infant and adult irises acquired on the same iris camera were compared. Matching performance was evaluated for four groups, infants 0 to 6 months, 7 to 12 months, 13 to 24 months, and adults. A mixed linear regression model was used to determine if infants’ genuine similarity scores changed over time. This study found that image quality metrics were different between infants and adults but in the older group, (13 to 24 months old) the image quality metric scores were more likely to be similar to adults. Infants 0 to 6 months old had worse performance at an FMR of 0.01% than infants 7 to 12 months, 13 to 24 months, and adults

BioTwist - overcoming severe distortions in ridge-based biometrics for successful identication

Biometrics rely on a physical trait's permanence and stability over time, as well as its individuality, robustness and ease to be captured. Challenges arise when working with newborns or infants because of the tininess and fragility of an infant's features, their uncooperative nature and their rapid growth. The last of these is particularly relevant when one tries to verify an infant's identity based on captures of a biometric taken at an earlier age. Finding a physical trait that is feasible for infants is often referred to as the infant biometric problem. This thesis explores the quality aspect of adult fingermarks and the correlation between image quality and the mark’s usefulness for an ongoing forensic investigation, and researches various aspects of the “ballprint” as an infant biometric. The ballprint, the friction ridge skin area of the foot pad under the big toe, exhibits similar properties to fingerprint but the ball possesses larger physical structures and a greater number of features. We collected a longitudinal ballprint database from 54 infants within 3 days of birth, at two months old, at 6 months and at 2 years. It has been observed that the skin of a newborn's foot dries and cracks so the ridge lines are often not visible to the naked eye and an adult fingerprint scanner cannot capture them. This thesis presents the physiological discovery that the ballprint grows isotropically during infancy and can be well approximated by a linear function of the infant's age. Fingerprint technology developed for adult fingerprints can match ballprints if they are adjusted by a physical feature (the inter-ridge spacing) to be of a similar size to adult fingerprints. The growth in ballprint inter-ridge spacing mirrors infant growth in terms of length/height. When growth is compensated for by isotropic rescaling, impressive verification scores are achieved even for captures taken 22 months apart. The scores improve even further when low-quality prints are rejected; the removal of the bottom third improves the Equal Error Rate from 1-2% to 0%. In conclusion, this thesis demonstrates that the ballprint is a feasible solution to the infant biometric problem

Image quality measures for fingerprint image enhancement

Abstract. Fingerprint image quality is an important factor in the performance of Automatic Fingerprint Identification Systems(AFIS). It is used to evaluate the system performance, assess enrollment acceptability, and evaluate fingerprint sensors. This paper presents a novel methodology for fingerprint image quality measurement. We propose limited ring-wedge spectral measure to estimate the global fingerprint image features, and inhomogeneity with directional contrast to estimate local fingerprint image features. Experimental results demonstrate the effectiveness of our proposal.