Analyzing the periocular biometric-based access control systems

Biometrics is a widely studied topic for security applications or identity identification. This project had focused on primarily on studying a small region around the eye known as the periocular region as a supplementary biometric. This is the region that includes eyelids, lashes and eyebrows. A few previous studies had proven that periocular biometrics has applied as an independent recognition system under unconstrained scenarios. The biometric data for this region can be easily obtained with existing setups used face and iris recognition. In this project, the data and information was gathered mainly through two methods which were the observation and review on other documentations related to the system. The qualitative research was performed through observation and surveys related to the system. The samples was obtained in a way that the cooperation from the subject or participant was informal while interaction with the biometric system was facilitated. the prototype generated alternative way to allow access to the system using the periocular biometric

Cross-Spectral Periocular Recognition with Conditional Adversarial Networks

This work addresses the challenge of comparing periocular images captured in different spectra, which is known to produce significant drops in performance in comparison to operating in the same spectrum. We propose the use of Conditional Generative Adversarial Networks, trained to con-vert periocular images between visible and near-infrared spectra, so that biometric verification is carried out in the same spectrum. The proposed setup allows the use of existing feature methods typically optimized to operate in a single spectrum. Recognition experiments are done using a number of off-the-shelf periocular comparators based both on hand-crafted features and CNN descriptors. Using the Hong Kong Polytechnic University Cross-Spectral Iris Images Database (PolyU) as benchmark dataset, our experiments show that cross-spectral performance is substantially improved if both images are converted to the same spectrum, in comparison to matching features extracted from images in different spectra. In addition to this, we fine-tune a CNN based on the ResNet50 architecture, obtaining a cross-spectral periocular performance of EER=1%, and GAR>99% @ FAR=1%, which is comparable to the state-of-the-art with the PolyU database.Comment: Accepted for publication at 2020 International Joint Conference on Biometrics (IJCB 2020

One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations

One weakness of machine-learning algorithms is the need to train the models for a new task. This presents a specific challenge for biometric recognition due to the dynamic nature of databases and, in some instances, the reliance on subject collaboration for data collection. In this paper, we investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition, a biometric recognition task. We analyze the outputs of CNN layers as identity-representing feature vectors. We examine the impact of Domain Adaptation on the network layers' output for unseen data and evaluate the method's robustness concerning data normalization and generalization of the best-performing layer. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images and fine-tuned for the target periocular dataset by utilizing out-of-the-box CNNs trained for the ImageNet Recognition Challenge and standard computer vision algorithms. For example, for the Cross-Eyed dataset, we could reduce the EER by 67% and 79% (from 1.70% and 3.41% to 0.56% and 0.71%) in the Close-World and Open-World protocols, respectively, for the periocular case. We also demonstrate that traditional algorithms like SIFT can outperform CNNs in situations with limited data or scenarios where the network has not been trained with the test classes like the Open-World mode. SIFT alone was able to reduce the EER by 64% and 71.6% (from 1.7% and 3.41% to 0.6% and 0.97%) for Cross-Eyed in the Close-World and Open-World protocols, respectively, and a reduction of 4.6% (from 3.94% to 3.76%) in the PolyU database for the Open-World and single biometric case.Comment: Submitted preprint to IEE Acces

UFPR-Periocular: A Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios

Recently, ocular biometrics in unconstrained environments using images obtained at visible wavelength have gained the researchers' attention, especially with images captured by mobile devices. Periocular recognition has been demonstrated to be an alternative when the iris trait is not available due to occlusions or low image resolution. However, the periocular trait does not have the high uniqueness presented in the iris trait. Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region. Also, to address the within-class variability caused by lighting and attributes in the periocular region, it is of paramount importance to use datasets with images of the same subject captured in distinct sessions. As the datasets available in the literature do not present all these factors, in this work, we present a new periocular dataset containing samples from 1,122 subjects, acquired in 3 sessions by 196 different mobile devices. The images were captured under unconstrained environments with just a single instruction to the participants: to place their eyes on a region of interest. We also performed an extensive benchmark with several Convolutional Neural Network (CNN) architectures and models that have been employed in state-of-the-art approaches based on Multi-class Classification, Multitask Learning, Pairwise Filters Network, and Siamese Network. The results achieved in the closed- and open-world protocol, considering the identification and verification tasks, show that this area still needs research and development

Efficient evolutionary-based neural architecture search in few GPU hours for image classification and medical image segmentation

Orientador: Lucas Ferrari de OliveiraTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 20/09/2021Inclui referências: p. 132-139Área de concentração: Ciência da ComputaçãoResumo: O uso de aprendizagem profunda (AP) está crescendo rapidamente, já que o poder computacional atual fornece otimização e inferência rápidas. Além disso, vários métodos exclusivos de AP estão evoluindo, permitindo resultados superiores em visão computacional, reconhecimento de voz e análise de texto. Os métodos AP extraem característica automaticamente para melhor representação de um problema específico, removendo o árduo trabalho do desenvolvimento de descritores de características dos métodos convencionais. Mesmo que esse processo sejaautomatizado, a criação inteligente de redes neurais é necessária para o aprendizado adequado da representação, o que requer conhecimento em AP. O campo de busca de arquiteturas neurais (BAN) foca no desenvolvimento de abordagens inteligentes que projetam redes robustas automaticamente para reduzir o conhecimento exigido para o desenvolvimento de redes eficientes. BAN pode fornecer maneiras de descobrir diferentes representações de rede, melhorando o estado da arte em diferentes aplicações. Embora BAN seja relativamente nova, várias abordagens foram desenvolvidas para descobrir modelos robustos. Métodos eficientes baseados em evolução são amplamente populares em BAN, mas seu alto consumo de placa gráfica (de alguns dias a meses)desencoraja o uso prático. No presente trabalho, propomos duas abordagens BAN baseadas na evolução eficiente com baixo custo de processamento, exigindo apenas algumas horas de processamento na placa gráfica (menos de doze em uma RTX 2080Ti) para descobrir modelos competitivos. Nossas abordagens extraem conceitos da programação de expressão gênica para representar e gerar redes baseadas em células robustas combinadas com rápido treinamento de candidatos, compartilhamento de peso e combinações dinâmicas. Além disso, os métodos propostos são empregados em um espaço de busca mais amplo, com mais células representando uma rede única. Nossa hipótese central é que BAN baseado na evolução pode ser usado em uma busca com baixo custo (combinada com uma estratégia robusta e busca eficiente) em diversas tarefas de visão computacional sem perder competitividade. Nossos métodos são avaliados em diferentes problemas para validar nossa hipótese: classificação de imagens e segmentação semântica de imagens médicas. Para tanto, as bases de dados CIFAR são estudadas para atarefa de classificação e o desafio CHAOS para a tarefa de segmentação. As menores taxas de erro encontradas nas bases CIFAR-10 e CIFAR-100 foram 2,17% ± 0,10 e 15,47% ± 0,51,respectivamente. Quanto às tarefas do desafio CHAOS, os valores de Dice ficaram entre 90% e96%. Os resultados obtidos com nossas propostas em ambas as tarefas mostraram a descoberta de redes robustas para ambas as tarefas com baixo custo na fase de busca, sendo competitivas em relação ao estado da arte em ambos os desafios.Abstract: Deep learning (DL) usage is growing fast since current computational power provides fast optimization and inference. Furthermore, several unique DL methods are evolving, enabling superior computer vision, speech recognition, and text analysis results. DL methods automatically extract features to represent a specific problem better, removing the hardworking of feature engineering from conventional methods. Even if this process is automated, intelligent network design is necessary for proper representation learning, which requires expertise in DL. The neural architecture search (NAS) field focuses on developing intelligent approaches that automatically design robust networks to reduce the expertise required for developing efficient networks. NAS may provide ways to discover different network representations, improving the state-of-the-art indifferent applications. Although NAS is relatively new, several approaches were developed for discovering robust models. Efficient evolutionary-based methods are widely popular in NAS, buttheir high GPU consumption (from a few days to months) discourages practical use. In the presentwork, we propose two efficient evolutionary-based NAS approaches with low-GPU cost, requiring only a few GPU hours (less than twelve in an RTX 2080Ti) to discover competitive models. Our approaches extract concepts from gene expression programming to represent and generate robust cell-based networks combined with fast candidate training, weight sharing, and dynamic combinations. Furthermore, the proposed methods are employed in a broader search space, withmore cells representing a unique network. Our central hypothesis is that evolutionary-based NAScan be used in a low-cost GPU search (combined with a robust strategy and efficient search) indiverse computer vision tasks without losing competitiveness. Our methods are evaluated indifferent problems to validate our hypothesis: image classification and medical image semantic segmentation. For this purpose, the CIFAR datasets are studied for the classification task andthe CHAOS challenge for the segmentation task. The lowest error rates found in CIFAR-10 andCIFAR-100 datasets were 2.17% ± 0.10 and 15.47% ± 0.51, respectively. As for the CHAOS challenge tasks, the dice scores were between 90% and 96%. The obtained results from our proposal in both tasks shown the discovery of robust networks for both tasks with little GPU costin the search phase, being competitive to state-of-the-art approaches in both challenges