Cross entropy clustering approach to iris segmentation for biometrics purpose

This work presents the step by step tutorial for how to use cross entropy clustering for the iris segmentation. We present the detailed construction of a suitable Gaussian model which best fits for in the case of iris images, and this is the novelty of the proposal approach. The obtained results are promising, both pupil and iris are extracted properly and all the information necessary for human identification and verification can be extracted from the found parts of the iris

A framework for biometric recognition using non-ideal iris and face

Off-angle iris images are often captured in a non-cooperative environment. The distortion of the iris or pupil can decrease the segmentation quality as well as the data extracted thereafter. Moreover, iris with an off-angle of more than 30° can have non-recoverable features since the boundary cannot be properly localized. This usually becomes a factor of limited discriminant ability of the biometric features. Limitations also come from the noisy data arisen due to image burst, background error, or inappropriate camera pixel noise. To address the issues above, the aim of this study is to develop a framework which: (1) to improve the non-circular boundary localization, (2) to overcome the lost features, and (3) to detect and minimize the error caused by noisy data. Non-circular boundary issue is addressed through a combination of geometric calibration and direct least square ellipse that can geometrically restore, adjust, and scale up the distortion of circular shape to ellipse fitting. Further improvement comes in the form of an extraction method that combines Haar Wavelet and Neural Network to transform the iris features into wavelet coefficient representative of the relevant iris data. The non-recoverable features problem is resolved by proposing Weighted Score Level Fusion which integrates face and iris biometrics. This enhancement is done to give extra distinctive information to increase authentication accuracy rate. As for the noisy data issues, a modified Reed Solomon codes with error correction capability is proposed to decrease intra-class variations by eliminating the differences between enrollment and verification templates. The key contribution of this research is a new unified framework for high performance multimodal biometric recognition system. The framework has been tested with WVU, UBIRIS v.2, UTMIFM, ORL datasets, and achieved more than 99.8% accuracy compared to other existing methods

Combining multiple Iris matchers using advanced fusion techniques to enhance Iris matching performance

M.Phil. (Electrical And Electronic Engineering)The enormous increase in technology advancement and the need to secure information e ectively has led to the development and implementation of iris image acquisition technologies for automated iris recognition systems. The iris biometric is gaining popularity and is becoming a reliable and a robust modality for future biometric security. Its wide application can be extended to biometric security areas such as national ID cards, banking systems such as ATM, e-commerce, biometric passports but not applicable in forensic investigations. Iris recognition has gained valuable attention in biometric research due to the uniqueness of its textures and its high recognition rates when employed on high biometric security areas. Identity veri cation for individuals becomes a challenging task when it has to be automated with a high accuracy and robustness against spoo ng attacks and repudiation. Current recognition systems are highly a ected by noise as a result of segmentation failure, and this noise factors increase the biometric error rates such as; the FAR and the FRR. This dissertation reports an investigation of score level fusion methods which can be used to enhance iris matching performance. The fusion methods implemented in this project includes, simple sum rule, weighted sum rule fusion, minimum score and an adaptive weighted sum rule. The proposed approach uses an adaptive fusion which maps feature quality scores with the matcher. The fused scores were generated from four various iris matchers namely; the NHD matcher, the WED matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching scores before fusion, raw scores were normalized using the tanh-estimators method, because it is e cient and robust against outliers. The results were tested against two publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric system measurements namely the AUC and the EER. The results of these two measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate (EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and with the EER = 0:038 for UBIRIS images

Development of Multirate Filter – Based Region Features for Iris Identification

The emergence of biometric system is seen as the next-generation technological solution in strengthening the social and national security. The evolution of biometrics has shifted the paradigm of authentication from classical token and knowledge-based systems to physiological and behavioral trait based systems. R & D on iris biometrics, in last one decade, has established it as one of the most promising traits. Even though, iris biometric takes high resolution near-infrared (NIR) images as input, its authentication accuracy is very commendable. Its performance is often influenced by the presence of noise, database size, and feature representation. This thesis focuses on the use of multi resolution analysis (MRA) in developing suitable features for non-ideal iris images. Our investigation starts with the iris feature extraction technique using Cohen −Daubechies − Feauveau 9/7 (CDF 9/7) filter bank. In this work, a technique has been proposed to deal with issues like segmentation failure and occlusion. The experimental studies deal with the superiority of CDF 9/7 filter bank over the frequency based techniques. Since there is scope for improving the frequency selectivity of CDF 9/7 filter bank, a tunable filter bank is proposed to extract region based features from non-cooperative iris images. The proposed method is based on half band polynomial of 14th order. Since, regularity and frequency selectivity are in inverse relationship with each other, filter coefficients are derived by not imposing maximum number of zeros. Also, the half band polynomial is presented in x-domain, so as to apply semidefinite programming, which results in optimization of coefficients of analysis/synthesis filter. The next contribution in this thesis deals with the development of another powerful MRA known as triplet half band filter bank (THFB). The advantage of THFB is the flexibility in choosing the frequency response that allows one to overcome the magnitude constraints. The proposed filter bank has improved frequency selectivity along with other desired properties, which is then used for iris feature extraction. The last contribution of the thesis describes a wavelet cepstral feature derived from CDF 9/7 filter bank to characterize iris texture. Wavelet cepstrum feature helps in reducing the dimensionality of the detail coefficients; hence, a compact feature presentation is possible with improved accuracy against CDF 9/7. The efficacy of the features suggested are validated for iris recognition on three publicly available databases namely, CASIAv3, UBIRISv1, and IITD. The features are compared with other transform domain features like FFT, Gabor filter and a comprehensive evaluation is done for all suggested features as well. It has been observed that the suggested features show superior performance with respect to accuracy. Among all suggested features, THFB has shown best performance

Visible, near infrared and thermal hand-based image biometric recognition

Biometric Recognition refers to the automatic identification of a person based on his or her anatomical characteristic or modality (i.e., fingerprint, palmprint, face) or behavioural (i.e., signature) characteristic. It is a fundamental key issue in any process concerned with security, shared resources, network transactions among many others. Arises as a fundamental problem widely known as recognition, and becomes a must step before permission is granted. It is supposed that protects key resources by only allowing those resources to be used by users that have been granted authority to use or to have access to them. Biometric systems can operate in verification mode, where the question to be solved is Am I who I claim I am? or in identification mode where the question is Who am I? Scientific community has increased its efforts in order to improve performance of biometric systems. Depending on the application many solutions go in the way of working with several modalities or combining different classification methods. Since increasing modalities require some user inconvenience many of these approaches will never reach the market. For example working with iris, face and fingerprints requires some user effort in order to help acquisition. This thesis addresses hand-based biometric system in a thorough way. The main contributions are in the direction of a new multi-spectral hand-based image database and methods for performance improvement. The main contributions are: A) The first multi-spectral hand-based image database from both hand faces: palmar and dorsal. Biometric database are a precious commodity for research, mainly when it offers something new like visual (VIS), near infrared (NIR) and thermography (TIR) images at a time. This database with a length of 100 users and 10 samples per user constitute a good starting point to check algorithms and hand suitability for recognition. B) In order to correctly deal with raw hand data, some image preprocessing steps are necessary. Three different segmentation phases are deployed to deal with VIS, NIR and TIR images specifically. Some of the tough questions to address: overexposed images, ring fingers and the cuffs, cold finger and noise image. Once image segmented, two different approaches are prepared to deal with the segmented data. These two approaches called: Holistic and Geometric define the main focus to extract the feature vector. These feature vectors can be used alone or can be combined in some way. Many questions can be stated: e.g. which approach is better for recognition?, Can fingers alone obtain better performance than the whole hand? and Is thermography hand information suitable for recognition due to its thermoregulation properties? A complete set of data ready to analyse, coming from the holistic and geometric approach have been designed and saved to test. Some innovative geometric approach related to curvature will be demonstrated. C) Finally the Biometric Dispersion Matcher (BDM) is used in order to explore how it works under different fusion schemes, as well as with different classification methods. It is the intention of this research to contrast what happen when using other methods close to BDM like Linear Discriminant Analysis (LDA). At this point, some interesting questions will be solved, e.g. by taking advantage of the finger segmentation (as five different modalities) to figure out if they can outperform what the whole hand data can teach us.El Reconeixement Biomètric fa referència a la identi cació automàtica de persones fent us d'alguna característica o modalitat anatòmica (empremta digital) o d'alguna característica de comportament (signatura). És un aspecte fonamental en qualsevol procés relacionat amb la seguretat, la compartició de recursos o les transaccions electròniques entre d'altres. És converteix en un pas imprescindible abans de concedir l'autorització. Aquesta autorització, s'entén que protegeix recursos clau, permeten així, que aquests siguin utilitzats pels usuaris que han estat autoritzats a utilitzar-los o a tenir-hi accés. Els sistemes biomètrics poden funcionar en veri cació, on es resol la pregunta: Soc jo qui dic que soc? O en identi cació on es resol la qüestió: Qui soc jo? La comunitat cientí ca ha incrementat els seus esforços per millorar el rendiment dels sistemes biomètrics. En funció de l'aplicació, diverses solucions s'adrecen a treballar amb múltiples modalitats o combinant diferents mètodes de classi cació. Donat que incrementar el número de modalitats, representa a la vegada problemes pels usuaris, moltes d'aquestes aproximacions no arriben mai al mercat. La tesis contribueix principalment en tres grans àrees, totes elles amb el denominador comú següent: Reconeixement biometric a través de les mans. i) La primera d'elles constitueix la base de qualsevol estudi, les dades. Per poder interpretar, i establir un sistema de reconeixement biomètric prou robust amb un clar enfocament a múltiples fonts d'informació, però amb el mínim esforç per part de l'usuari es construeix aquesta Base de Dades de mans multi espectral. Les bases de dades biomètriques constitueixen un recurs molt preuat per a la recerca; sobretot si ofereixen algun element nou com es el cas. Imatges de mans en diferents espectres electromagnètics: en visible (VIS), en infraroig (NIR) i en tèrmic (TIR). Amb un total de 100 usuaris, i 10 mostres per usuari, constitueix un bon punt de partida per estudiar i posar a prova sistemes multi biomètrics enfocats a les mans. ii) El segon bloc s'adreça a les dues aproximacions existents en la literatura per a tractar les dades en brut. Aquestes dues aproximacions, anomenades Holística (tracta la imatge com un tot) i Geomètrica (utilitza càlculs geomètrics) de neixen el focus alhora d'extreure el vector de característiques. Abans de tractar alguna d'aquestes dues aproximacions, però, és necessària l'aplicació de diferents tècniques de preprocessat digital de la imatge per obtenir les regions d'interès desitjades. Diferents problemes presents a les imatges s'han hagut de solucionar de forma original per a cadascuna de les tipologies de les imatges presents: VIS, NIR i TIR. VIS: imatges sobre exposades, anells, mànigues, braçalets. NIR: Ungles pintades, distorsió en forma de soroll en les imatges TIR: Dits freds La segona àrea presenta aspectes innovadors, ja que a part de segmentar la imatge de la ma, es segmenten tots i cadascun dels dits (feature-based approach). Així aconseguim contrastar la seva capacitat de reconeixement envers la ma de forma completa. Addicionalment es presenta un conjunt de procediments geomètrics amb la idea de comparar-los amb els provinents de l'extracció holística. La tercera i última àrea contrasta el procediment de classi cació anomenat Biometric Dispersion Matcher (BDM) amb diferents situacions. La primera relacionada amb l'efectivitat respecte d'altres mètode de reconeixement, com ara l'Anàlisi Lineal Discriminant (LDA) o bé mètodes com KNN o la regressió logística. Les altres situacions que s'analitzen tenen a veure amb múltiples fonts d'informació, quan s'apliquen tècniques de normalització i/o estratègies de combinació (fusió) per millorar els resultats. Els resultats obtinguts no deixen lloc per a la confusió, i són certament prometedors en el sentit que posen a la llum la importància de combinar informació complementària per obtenir rendiments superiors

Combining multiple Iris matchers using advanced fusion techniques to enhance Iris matching performance

M.Phil. (Electrical And Electronic Engineering)The enormous increase in technology advancement and the need to secure information e ectively has led to the development and implementation of iris image acquisition technologies for automated iris recognition systems. The iris biometric is gaining popularity and is becoming a reliable and a robust modality for future biometric security. Its wide application can be extended to biometric security areas such as national ID cards, banking systems such as ATM, e-commerce, biometric passports but not applicable in forensic investigations. Iris recognition has gained valuable attention in biometric research due to the uniqueness of its textures and its high recognition rates when employed on high biometric security areas. Identity veri cation for individuals becomes a challenging task when it has to be automated with a high accuracy and robustness against spoo ng attacks and repudiation. Current recognition systems are highly a ected by noise as a result of segmentation failure, and this noise factors increase the biometric error rates such as; the FAR and the FRR. This dissertation reports an investigation of score level fusion methods which can be used to enhance iris matching performance. The fusion methods implemented in this project includes, simple sum rule, weighted sum rule fusion, minimum score and an adaptive weighted sum rule. The proposed approach uses an adaptive fusion which maps feature quality scores with the matcher. The fused scores were generated from four various iris matchers namely; the NHD matcher, the WED matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching scores before fusion, raw scores were normalized using the tanh-estimators method, because it is e cient and robust against outliers. The results were tested against two publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric system measurements namely the AUC and the EER. The results of these two measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate (EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and with the EER = 0:038 for UBIRIS images

Investigation of iris recognition in the visible spectrum

mong the biometric systems that have been developed so far, iris recognition systems have emerged as being one of the most reliable. In iris recognition, most of the research was conducted on operation under near infrared illumination. For unconstrained scenarios of iris recognition systems, the iris images are captured under visible light spectrum and therefore incorporate various types of imperfections. In this thesis the merits of fusing information from various sources for improving the state of the art accuracies of colour iris recognition systems is evaluated. An investigation of how fundamentally different fusion strategies can increase the degree of choice available in achieving certain performance criteria is conducted. Initially, simple fusion mechanisms are employed to increase the accuracy of an iris recognition system and then more complex fusion architectures are elaborated to further enhance the biometric system’s accuracy. In particular, the design process of the iris recognition system with reduced constraints is carried out using three different fusion approaches: multi-algorithmic, texture and colour fusion and multiple classifier systems. In the first approach, one novel iris feature extraction methodology is proposed and a multi-algorithmic iris recognition system using score fusion, composed of 3 individual systems, is benchmarked. In the texture and colour fusion approach, the advantages of fusing information from the iris texture with data extracted from the eye colour are illustrated. Finally, the multiple classifier systems approach investigates how the robustness and practicability of an iris recognition system operating on visible spectrum images can be enhanced by training individual classifiers on different iris features. Besides the various fusion techniques explored, an iris segmentation algorithm is proposed and a methodology for finding which colour channels from a colour space reveal the most discriminant information from the iris texture is introduced. The contributions presented in this thesis indicate that iris recognition systems that operate on visible spectrum images can be designed to operate with an accuracy required by a particular application scenario. Also, the iris recognition systems developed in the present study are suitable for mobile and embedded implementations