Speeded Up Robust Features Descriptor for Iris Recognition Systems

اكتسبت النظم البايومترية اهتماما كبيرا لعدة تطبيقات. كان تحديد القزحية أحد أكثر التقنيات البايومترية تطوراً للمصادقة الفعالة. نظام التعرف على القزحية الحالية يقدم نتائج دقيقة وموثوق بها على أساس الصور المأخوذة بالأشعة التحت الحمراء (NIR) عندما يتم التقاط الصور في مسافة ثابتة مع تعاون المستخدم. ولكن بالنسبة لصور العين الملونة التي تم الحصول عليها تحت الطول الموجي المرئي (VW) دون التعاون بين المستخدمين، فإن كفاءة التعرف على القزحية تتأثر بسبب الضوضاء مثل صور عدم وضوح العين، و تداخل الرموش ، والانسداد  بالأجفان وغيرها. يهدف هذا العمل إلى استخدام (SURF) لاسترداد خصائص القزحية في كل من صور قزحية NIR والطيف المرئي. يتم استخدام هذا النهج وتقييمه على قواعد بيانات CASIA v1and IITD v1 كصورة قزحية NIR وUBIRIS v1 كصورة ملونة. وأظهرت النتائج معدل دقة عالية (98.1 ٪) على CASIA v1, (98.2) على IITD v1 و (83٪) على UBIRIS v1 تقييمها بالمقارنة مع الأساليب الأخرى.Biometric systems have gained significant attention for several applications. Iris identification was one of the most sophisticated biometrical techniques for effective and confident authentication. Current iris identification system offers accurate and reliable results based on near- infra -red light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without cooperation between the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion and reflection. This works aims to use Speeded up robust features Descriptor (SURF) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. This approach is used and evaluated on the CASIA v1and IITD v1 databases as NIR iris image and UBIRIS v1 as color image. The evaluation results showed a high accuracy rate 98.1 % on CASIA v1, 98.2 on IITD v1 and 83% on UBIRIS v1 evaluated by comparing to the other method

Optimized biometric system based iris-signature for human identification

This research aimed at comparing iris-signature techniques, namely the Sequential Technique (ST) and the Standard Deviation Technique (SDT). Both techniques were measured by Backpropagation (BP), Probabilistic, Radial basis function (RBF), and Euclidian distance (ED) classifiers. A biometric system-based iris is developed to identify 30 of CASIA-v1 and 10 subjects from the Real-iris datasets. Then, the proposed unimodal system uses Fourier descriptors to extract the iris features and represent them as an iris-signature graph. The 150 values of input machine vector were optimized to include only high-frequency coefficients of the iris-signature, then the two optimization techniques are applied and compared. The first optimization (ST) selects sequentially new feature values with different lengths from the enrichment graph region that has rapid frequency changes. The second technique (SDT) chooses the high variance coefficients as a new feature of vectors based on the standard deviation formula. The results show that SDT achieved better recognition performance with the lowest vector-lengths, while Probabilistic and BP have the best accuracy

Iris Feature Detection Using Split Block And PSO For Iris Identification System

The past decade has seen the rapid development of iris identification in many approaches to identify unique iris features such as crypts. However, it is noted that, unique iris features change due to iris aging, diet or human health conditions. The changing of iris features creates the mismatch in comparison phase to determine either genuine or not genuine. Therefore, to determine genuinely, this study proposes a new model of iris recognition using combinational approach of a split block and particle swarm optimization (PSO) in selecting the best crypt among unique iris features template. The split block has been used in this study to separate the image with the part that very important in the iris template meanwhile, the particles in PSO searches the most optimal crypt features in the iris. The results indicate an improvement of PSNR rates, which is 23.886 dB and visually improved quality of crypts for iris identification. The significance of this study contributes to a new method of feature extraction using bio-inspired, which enhanced the ability of detection in iris identification

A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features

In this paper we propose a novel visual method for protein model classification and retrieval. Different from the conventional methods, the key idea of the proposed method is to extract image features of proteins and measure the visual similarity between proteins. Firstly, the multiview images are captured by vertices and planes of a given octahedron surrounding the protein. Secondly, the local features are extracted from each image of the different views by the SURF algorithm and are vector quantized into visual words using a visual codebook. Finally, KLD is employed to calculate the similarity distance between two feature vectors. Experimental results show that the proposed method has encouraging performances for protein retrieval and categorization as shown in the comparison with other methods

A Supervised Face Recognition in Still Images using Interest Points

In recent decades, face recognition (FR) has been studied due to technologicaladvances and increased computational power of equipments. This happens also by the emergence of concern with security issues, and the possibility of its application in various domains. In this context, this study was developed in order to present an approach for recognition of individuals through facial images. For this, we used interest points detectors called SIFT (Scale Invariant Feature Transform) and SURF (Speeded up Robust Features), which are invariant to certain complicating factors found in the recognition process, such as lighting changes, scale and rotation. Using the face images of 138 individuals, the results obtained from the experiments show that the approach is suitable for face recognition

Integration of biometrics and steganography: A comprehensive review

The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards

A solution based on iris recognition for data protection on mobile devices

Orientadores: Leandro Aparecido Villas, Fabio Augusto FariaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O uso de dispositivos móveis em atividades diárias, tais como, acessar a serviços bancários, efetuar compras on-line e trocar mensagens via e-mail ou redes sociais, tornou-se mais frequente na vida das pessoas. Assegurar a privacidade e a segurança dos dados contidos nesses dispositivos, é essencial para incentivar o surgimento de novas aplicações e aumentar a adesão de novos usuários. Para este fim, a utilização de biometria tem sido muito atraente, devido o alto grau de dissimilaridade entre as propriedades biológicas de indivíduos distintos. Em particular, a biometria por meio de íris destaca-se dentre as demais técnicas de biometria, por possuir um padrão de textura rico em detalhes que permanecem estáveis ao longo da vida, diferentemente da impressão digital e da face que estão sujeitas a grandes variações. Além disso, não há necessidade de hardware adicional, uma vez que o sensor presente na maioria dos dispositivos móveis é suficiente para capturar as imagens de íris a distância, sem haver contato direto do indivíduo com o sensor (menos intrusiva). Contudo, lidar com imagens de íris capturadas por usuários ingênuos (proprietários de dispositivos móveis) e em ambientes não controlados torna o reconhecimento de íris uma tarefa desafiadora, e por isso, requer métodos mais robustos para desempenhar essa tarefa. Este trabalho apresenta uma solução baseada em descritores binários de pontos-chave (ou keypoints) para realizar a codificação da textura de íris, onde apenas os pixels entorno dos keypoints são utilizados na codificação, por considerar que alguns pixels são mais estáveis do que outros, e desta forma, é reduzido o impacto causado pela fragilidade de bits, problema frequente em abordagens que utilizam todos os pixels para gerar a codificação. São comparados três descritores binários de keypoints bem conhecidos (BRIEF, ORB e BRISK) para identificar qual é o mais adequado para ser utilizado na codificação da textura de íris. Em comum, os três descritores são eficientes no uso de memória e no tempo de processamento, e possuem bom desempenho para aplicações de reconhecimento. Além disso, são comparados três diferentes métodos de segmentação existentes na literatura para avaliar o impacto causado no desempenho do reconhecimento. São utilizados dois conjuntos de imagens de íris amplamente conhecidos na literatura, chamados CASIA e MICHE-I, para avaliação da solução proposta. Os resultados das simulações apresentaram um bom desempenho, considerando as métricas de eficácia, uso de memória e tempo de processamentoAbstract: The use of mobile devices in daily activities, such as access to banking services, online buying, send/receive emails or interaction on social networks, has become more often on people¿s lives. To ensure the privacy and security of the data contained on these devices it is essential to encourage the emergence of new applications and increase users involvement. To this end, biometrics has been very attractive due to the high dissimilarity among biological properties of distinct individuals. In particular, iris recognition stands out among other biometric techniques. The iris presents a rich texture pattern that remains stable throughout our life. On the other hand, other biometric techniques are subject to large variations. Furthermore, no additional hardware is required, since that sensor already present in most mobile devices is sufficient to capture the iris image at distance, without any direct contact with the sensor (less intrusive). However, dealing with samples captured by naive users (owners of mobile devices) and uncontrolled settings make iris recognition a challenging task, and therefore requires more robust methods to address them. In this work we present a solution based on binary keypoint descriptors to perform iris encoding. In our solution, only surrounding pixels of keypoints are used, considering that some pixels are more stable than others, and thus, the impact caused by bit fragility is reduced, which is a frequent problem in approaches that use all the pixels to generate the coding. Three well known binary keypoint descriptors - BRIEF, ORB and BRISK - are compared to identify which is the most suitable for use on the iris encoding. In common, all of them are efficient and have good performance for recognition applications. Moreover, three different segmentation methods are compared to assess the impact on recognition performance. We used two iris datasets widely known in literature, called CASIA and MICHE-I, to assess the proposed solution. The simulation results shown a good performance considering the metrics effectiveness, memory usage and processing timeMestradoCiência da ComputaçãoMestre em Ciência da Computação4716.4Funcam

A contribution for single and multiple faces recognition using feature-based approaches

Among biometric recognition systems, face biometrics plays an important role in research activities and security applications since face images can be acquired without any knowledge of individuals. Nowadays a huge amount of digital images and video sequences have been acquired mainly from uncontrolled conditions, frequently including noise, blur, occlusion and variation on scale and illumination. Because of these issues, face recognition (FR) is still an active research area and becomes a complex problem and a challenging task. In this context, the motivation comes from the fact that recognition of faces in digital images with complex background and databases of face images have become one of the successful applications of Computer Vision. Hence, the main goal of this work is to recognize one or more faces from still images with multiple faces and from a database of single faces obtained under different conditions. To work with multiple face images under varying conditions, a semi-supervised approach proposed based on the invariant and discriminative power of local features. The extraction of local features is done using Speeded-Up Robust Features (SURF). The search for regions from which optimal features can be extracted is fulfilled by an improved ABC algorithm. To fully exploit the proposed approach, an extensive experimental analysis was performed. Results show that this approach is robust and efficient for face recognition applications except for faces with non-uniform illumination. In the literature, a significant number of single FR researches are based on extraction of only one feature and machine learning approaches. Besides, existing feature extraction approaches broadly use either global or local features. To obtain relevant and complementary features from face images, a face recognition methodology should consider heterogeneous features and semi-global features. Therefore, a novel hierarchical semi-supervised FR approach is proposed based on extraction of global, semi-global and local features. Global and semi-global features are extracted using Color Angles (CA) and edge histogram descriptors (EHD) meanwhile only local features are extracted using SURF. An extensive experimental analysis using the three feature extraction methods was done first individually followed by a three-stage hierarchical scheme using the face images obtained under two different lighting conditions with facial expression and slight scale variation. Furthermore, the performance of the approach was also analyzed using global, semi-global and local features combinations for CA and EHD. The proposed approach achieves high recognition rates considering all image conditions tested in this work. In addition to this, the results emphasize the influence of local and semi-global features in the recognition performance. In both, single face and multiple faces approaches, the main achievement is the high performance obtained only from the discriminative capacity of extracted features without any training schemes.Entre os sistemas de reconhecimento biométrico, a biometria da face exerce um papel importante nas atividades de pesquisa e nas aplicações de segurança, pois a face pode ser obtida sem conhecimento prévio de um indivíduo. Atualmente, uma grande quantidade de imagens digitais e seqüências de vídeo têm sido adquiridas principalmente sob condições não-controladas, freqüentemente com ruído, borramento, oclusão e variação de escala e iluminação. Por esses problemas, o reconhecimento facial (RF) é ainda considerado como uma área de pesquisa ativa e uma tarefa desafiadora. A motivação vem do fato que o reconhecimento de faces nas imagens com fundo complexo e em base de imagens faciais tem sido uma aplicação de sucesso. Portanto, o principal foco deste trabalho é reconhecer uma ou mais faces em imagens estáticas contendo diversos indivíduos e um individuo (face) em uma base de imagens com faces únicas obtidas sob condições diferentes. Para trabalhar com faces múltiplas, uma abordagem semi-supervisionada foi proposta baseada em características locais invariantes e discriminativas. A extração de características (EC) locais é feita utilizando-se do algoritmo Speeded-Up Robust Features (SURF). A busca por regiões nas quais as características ótimas podem ser extraídas é atendida através do algoritmo ABC. Os resultados obtidos mostram que esta abordagem é robusta e eficiente para aplicações de RF exceto para faces com iluminação não-uniforme. Muitos trabalhos de RF são baseados somente na extração de uma característica e nas abordagens de aprendizagem de máquina. Além disso, as abordagens existentes de EC usam características globais e/ou locais. Para obter características relevantes e complementares, a metodologia de RF deve considerar também as características de diferentes tipos e semi-globais. Portanto, a abordagem hierárquica de RF é proposta baseada na EC como globais, semi-globais e locais. As globais e semi-globais são extraídas utilizando-se de Color Angles (CA) e Edge Histogram Descriptors (EHD) enquanto somente características locais são extraídas utilizando-se do SURF. Uma ampla análise experimental foi feita utilizando os três métodos individualmente, seguido por um esquema hierárquico de três - estágios usando imagens faciais obtidas sob duas condições diferentes de iluminação com expressão facial e uma variação de escala leve. Além disso, para CA e EHD, o desempenho da abordagem foi também analisado combinando-se características globais, semi-globais e locais. A abordagem proposta alcança uma taxa de reconhecimento alta com as imagens de todas as condições testadas neste trabalho. Os resultados enfatizam a influência das características locais e semi-globais no desempenho do reconhecimento. Em ambas as abordagens, tanto nas faces únicas quanto nas faces múltiplas, a conquista principal é o alto desempenho obtido somente com a capacidade discriminativa de características sem nenhum esquema de treinamento

A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Higher Committee for Education Development in Ira