An enhanced iris recognition and authentication system using energy measure

In order to fight identity fraud, the use of a reliable personal identifier has become a necessity. Using Personal Identification Number (PIN) or a password is no longer secure enough to identify an individual. Iris recognition is considered to be one of the best and accurate form of biometric measurements compared to others, it has become an interesting research area. Iris recognition and authentication has a major issue in its code generation and verification accuracy, in order to enhance the authentication process, a binary bit sequence of iris is generated, which contain several vital information that is used to calculate the Mean Energy and Maximum Energy that goes into the eye with an adopted Threshold Value. The information generated can further be used to find out different eye ailments. An iris is obtained using a predefined iris image which is scanned through eight (8) different stages and wavelet packet decomposition is used to generate 64 wavelet packages bit iris code so as to match the iris codes with Hamming distance criteria and evaluate different energy values. The system showed 98% True Acceptance Rate and 1% False Rejection Rate and this is because some of the irises weren’t properly captured during iris acquisition phase. The system is implemented using UBIRIS v.1 Database.Keywords: Local Image Properties, Authentication Enhancement, Iris Authentication, Local Image, Iris Recognition, Binary Bit Sequenc

Reconhecimento biométrico da íris na região de comprimentos de onda do infravermelho próximo e do visível

Neste trabalho é proposta e concretizada a criação de uma base de dados com imagens da íris adquiridas e registadas de forma cooperativa em condições controladas de iluminação. A base de dados criada integra imagens da íris adquiridas simultaneamente nas regiões do visível (RGB) e infravermelho próximo (NIR) do espectro electromagnético. São ainda propostos e testados dois métodos de reconhecimento da íris com base na informação contida nos quatro canais RGB e NIR um dos métodos consiste num modelo linear cujos coeficientes foram calculados em função da base de dados criada enquanto que o outro consiste num modelo neuronal treinado com dados da mesma base de dados. Após a segmentação e normalização das imagens das íris procede-se à optimização dos parâmetros de filtros de Gabor a aplicar às regiões visível e infravermelho próximo do espectro, obtendo a informação relativa aos quatro canais: vermelho, verde, azul (Red, Green e Blue - RGB) e ao canal do infravermelho próximo (Near Infrared - NIR). Estes filtros optimizados são usados para a codificação final das imagens segmentadas das íris a cada um dos quatro canais anteriormente referidos, o desempenho da codificação das íris é analisado em cada um destes canais em separado. Por fim, foram criados dois modelos de reconhecimento da íris, um linear e outro utilizando redes neuronais que fundem a informação dos canais do visível RGB, ou em alternativa usam a informação associada aos quatro canais RGB e NIR. Os resultados mostraram que o modelo que tem o melhor desempenho no reconhecimento da íris é o modelo linear em que a informação dos quatro canais RGB e NIR é usada.In this paper is proposed and implemented the creation of a database for iris images acquired and registered in a cooperative way under controlled conditions of illumination. The created database includes iris images acquired simultaneously in the regions of the visible (RGB) and near infrared (NIR) of the electromagnetic spectrum. It’s also proposed and tested two methods for iris recognition with the information contained in the four acquired channels RGB and NIR. The first method is a linear model whose coefficients were calculated according to the created database while the other method consists of a neuronal model trained with the data from the same database. After iris images segmentation and normalization the next step was the optimization of the Gabor filters parameters to be applied to corresponding segmented iris images in the visible and near infrared spectrum, obtaining the information in the four channels: red, green, blue (Red, Green and Blue - RGB) and near-infrared channel (Near infrared - NIR). These optimal filters were then used to encode the segmented images of the iris on each of the four channels mentioned above. The performance of the encoding was also analyzed in each channel separately. Finally, two iris recognition models were built, the first was a linear model and the second uses a neural network that merge information from the visible RGB channels, or alternatively uses the information association of all of the four channels RGB and NIR. The results show that the model that has the better performance in iris recognition is the linear model which holds the information of the four channels RGB and NIR.In this paper is proposed and implemented the creation of a database for iris images acquired and registered in a cooperative way under controlled conditions of illumination. The created database includes iris images acquired simultaneously in the regions of the visible (RGB) and near infrared (NIR) of the electromagnetic spectrum. It’s also proposed and tested two methods for iris recognition with the information contained in the four acquired channels RGB and NIR. The first method is a linear model whose coefficients were calculated according to the created database while the other method consists of a neuronal model trained with the data from the same database. After iris images segmentation and normalization the next step was the optimization of the Gabor filters parameters to be applied to corresponding segmented iris images in the visible and near infrared spectrum, obtaining the information in the four channels: red, green, blue (Red, Green and Blue - RGB) and near-infrared channel (Near infrared - NIR). These optimal filters were then used to encode the segmented images of the iris on each of the four channels mentioned above. The performance of the encoding was also analyzed in each channel separately. Finally, two iris recognition models were built, the first was a linear model and the second uses a neural network that merge information from the visible RGB channels, or alternatively uses the information association of all of the four channels RGB and NIR. The results show that the model that has the better performance in iris recognition is the linear model which holds the information of the four channels RGB and NIR

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