Deep neural networks for image quality: a comparison study for identification photos

Many online platforms allow their users to upload images to their account profile. The fact that a user is free to upload any image of their liking to a university or a job platform, has resulted in occurrences of profile images that weren’t very professional or adequate in any of those contexts. Another problem associated with submitting a profile image is that even if there is some kind of control over each submitted image, this control is performed manually by someone, and that process alone can be very tedious and time-consuming, especially when there are cases of a large influx of new users joining those platforms. Based on international compliance standards used to validate photographs for machine-readable travel documents, there are SDKs that already perform automatic classification of the quality of those photographs, however, the classification is based on traditional computer vision algorithms. With the growing popularity and powerful performance of deep neural networks, it would be interesting to examine how would these perform in this task. This dissertation proposes a deep neural network model to classify the quality of profile images, and a comparison of this model against traditional computer vision algorithms, with respect to the complexity of the implementation, the quality of the classifications, and the computation time associated to the classification process. To the best of our knowledge, this dissertation is the first to study the use of deep neural networks on image quality classification.Muitas plataformas online permitem que os seus utilizadores façam upload de imagens para o perfil das respetivas contas. O facto de um utilizador ser livre de submeter qualquer imagem do seu agrado para uma plataforma de universidade ou de emprego, pode resultar em ocorrências de casos onde as imagens de perfil não são adequadas ou profissionais nesses contextos. Outro problema associado à submissão de imagens para um perfil é que, mesmo que haja algum tipo de controlo sobre cada imagem submetida, esse controlo é feito manualmente. Esse processo, por si, só pode ser aborrecido e demorado, especialmente em situações de grande afluxo de novos utilizadores a inscreverem-se nessas plataformas. Com base em normas internacionais utilizadas para validar fotografias de documentos de viagem de leitura óptica, existem SDKs que já realizam a classificação automática da qualidade dessas fotografias. No entanto, essa classificação é baseada em algoritmos tradicionais de visão computacional. Com a crescente popularidade e o poderoso desempenho de redes neurais profundas, seria interessante examinar como é que estas se comportam nesta tarefa. Assim, esta dissertação propõe um modelo de rede neural profunda para classificar a qualidade de imagens de perfis e faz uma comparação deste modelo com algoritmos tradicionais de visão computacional, no que respeita à complexidade da implementação, qualidade das classificações e ao tempo de computação associado ao processo de classificação. Tanto quanto sabemos, esta dissertação é a primeira a estudar o uso de redes neurais profundas na classificação da qualidade de imagem

FACE IMAGE DE-OCCLUSION WITH VARIABLE-THRESHOLD ROBUST PCA

Master'sMASTER OF SCIENC

Face comparison in forensics:A deep dive into deep learning and likelihood rations

This thesis explores the transformative potential of deep learning techniques in the field of forensic face recognition. It aims to address the pivotal question of how deep learning can advance this traditionally manual field, focusing on three key areas: forensic face comparison, face image quality assessment, and likelihood ratio estimation. Using a comparative analysis of open-source automated systems and forensic experts, the study finds that automated systems excel in identifying non-matches in low-quality images, but lag behind experts in high-quality settings. The thesis also investigates the role of calibration methods in estimating likelihood ratios, revealing that quality score-based and feature-based calibrations are more effective than naive methods. To enhance face image quality assessment, a multi-task explainable quality network is proposed that not only gauges image quality, but also identifies contributing factors. Additionally, a novel images-to-video recognition method is introduced to improve the estimation of likelihood ratios in surveillance settings. The study employs multiple datasets and software systems for its evaluations, aiming for a comprehensive analysis that can serve as a cornerstone for future research in forensic face recognition

Explainable and Interpretable Face Presentation Attack Detection Methods

Decision support systems based on machine learning (ML) techniques are excelling in most artificial intelligence (AI) fields, over-performing other AI methods, as well as humans. However, challenges still exist that do not favour the dominance of AI in some applications. This proposal focuses on a critical one: lack of transparency and explainability, reducing trust and accountability of an AI system. The fact that most AI methods still operate as complex black boxes, makes the inner processes which sustain their predictions still unattainable. The awareness around these observations foster the need to regulate many sensitive domains where AI has been applied in order to interpret, explain and audit the reliability of the ML based systems. Although modern-day biometric recognition (BR) systems are already benefiting from the performance gains achieved with AI (which can account for and learn subtle changes in the person to be authenticated or statistical mismatches between samples), it is still in the dark ages of black box models, without reaping the benefits of the mismatches between samples), it is still in the dark ages of black box models, without reaping the benefits of the XAI field. This work will focus on studying AI explainability in the field of biometrics focusing in particular use cases in BR, such as verification/ identification of individuals and liveness detection (LD) (aka, antispoofing). The main goals of this work are: i) to become acquainted with the state-of-the-art in explainability and biometric recognition and PAD methods; ii) to develop an experimental work xxxxx Tasks 1st semester (1) Study of the state of the art- bibliography review on state of the art for presentation attack detection (2) Get acquainted with the previous work of the group in the topic (3) Data preparation and data pre-processing (3) Define the experimental protocol, including performance metrics (4) Perform baseline experiments (5) Write monography Tasks 2nd semester (1) Update on the state of the art (2) Data preparation and data pre-processing (3) Propose and implement a methodology for interpretability in biometrics (4) Evaluation of the performance and comparison with baseline and state of the art approaches (5) Dissertation writing Referências bibliográficas principais: (*) [Doshi17] B. Kim and F. Doshi-Velez, "Interpretable machine learning: The fuss, the concrete and the questions," 2017 [Mol19] Christoph Molnar. Interpretable Machine Learning. 2019 [Sei18] C. Seibold, W. Samek, A. Hilsmann, and P. Eisert, "Accurate and robust neural networks for security related applications exampled by face morphing attacks," arXiv preprint arXiv:1806.04265, 2018 [Seq20] Sequeira, Ana F., João T. Pinto, Wilson Silva, Tiago Gonçalves and Cardoso, Jaime S., "Interpretable Biometrics: Should We Rethink How Presentation Attack Detection is Evaluated?", 8th IWBF2020 [Wilson18] W. Silva, K. Fernandes, M. J. Cardoso, and J. S. Cardoso, "Towards complementary explanations using deep neural networks," in Understanding and Interpreting Machine Learning in MICA. Springer, 2018 [Wilson19] W. Silva, K. Fernandes, and J. S. Cardoso, "How to produce complementary explanations using an Ensemble Model," in IJCNN. 2019 [Wilson19A] W. Silva, M. J. Cardoso, and J. S. Cardoso, "Image captioning as a proxy for Explainable Decisions" in Understanding and Interpreting Machine Learning in MICA, 2019 (Submitted

Human face recognition under degraded conditions

Comparative studies on the state of the art feature extraction and classification techniques for human face recognition under low resolution problem, are proposed in this work. Also, the effect of applying resolution enhancement, using interpolation techniques, is evaluated. A gradient-based illumination insensitive preprocessing technique is proposed using the ratio between the gradient magnitude and the current intensity level of image which is insensitive against severe level of lighting effect. Also, a combination of multi-scale Weber analysis and enhanced DD-DT-CWT is demonstrated to have a noticeable stability versus illumination variation. Moreover, utilization of the illumination insensitive image descriptors on the preprocessed image leads to further robustness against lighting effect. The proposed block-based face analysis decreases the effect of occlusion by devoting different weights to the image subblocks, according to their discrimination power, in the score or decision level fusion. In addition, a hierarchical structure of global and block-based techniques is proposed to improve the recognition accuracy when different image degraded conditions occur. Complementary performance of global and local techniques leads to considerable improvement in the face recognition accuracy. Effectiveness of the proposed algorithms are evaluated on Extended Yale B, AR, CMU Multi-PIE, LFW, FERET and FRGC databases with large number of images under different degradation conditions. The experimental results show an improved performance under poor illumination, facial expression and, occluded images

Advanced Techniques for Face Recognition under Challenging Environments

Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 402)

This bibliography lists 244 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Nov. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Usability in biometric recognition systems

Mención Internacional en el título de doctorBiometric recognition, which is a technology already mature, grows nowadays in several contexts, including forensics, access controls, home automation systems, internet, etc. Now that technology is moving to mobile scenarios, biometric recognition is being also integrated in smartphones, tablets and other mobile devices as a convenient solution for guaranteeing security, complementing other methods such as PIN or passwords. Nevertheless, the use of biometric recognition is not as spread as desired and it is still unknown for a wide percentage of the population. It has been demonstrated [1] that some of the possible reasons for the slow penetration of biometrics could be related to usability concerns. This could lead to various drawbacks like worst error rates due to systems misuses and it could end with users rejecting the technology and preferring other approaches. This Thesis is intended to cover this topic including a study of the current state of the art, several experiments analysing the most relevant usability factors and modifications to a usability evaluation methodology. The chosen methodology is the H-B interaction, carried out by Fernandez-Saavedra [2], based on the ISO/IEC 19795 [3], the HBSI [4], the ISO 9241-210 [5] and on Common Criteria [6]. Furthermore, this work is focused on dealing with accessibility concerns in biometric recognition systems. This topic, usually included into the usability field, has been addressed here separately, though the study of the accessibility has followed the same steps as the usability study: reviewing the state of the art, pointing and analysing the main influential factors and making improvements to the state of the art. The recently published standard EN 301 549 – “Accessibility requirements suitable for public procurement of ICT products and services in Europe” [7] has been also analysed. These two topics have been overcome through the well-known user-centric-design approach. In this way, first the influential factors have been detected. Then, they have been isolated (when possible) and measured. The results obtained have been then interpreted to suggest new updates to the H-B interaction. This 3-steps approach has been applied cyclically and the factors and methodology updated after each iteration. Due to technology and usability trends, during this work, all the systems/applications developed in the experiments have been thought to be mobile directly or indirectly. The biometric modalities used during the experiments performed in this Thesis are those pointed as suitable for biometric recognition in mobile devices: handwritten recognition signature, face and fingerprint recognition. Also, the scenarios and the applications used are in line with the main uses of biometrics in mobile environments, such as sign documents, locking/unlocking devices, or make payments. The outcomes of this Thesis are intended to guide future developers in the way of designing and testing proper usable and accessible biometrics. Finally, the results of this Thesis are being suggested as a new International Standard within ISO/IEC/JTC1/SC37 – Biometric Recognition, as standardization is the proper way of guaranteeing usability and accessibility in future biometric systems. The contributions of this Thesis include: • Improvements to the H-B interaction methodology, including several usability evaluations. • Improvements on the accessibility of the ICT (Information and Communications Technology) products by means of the integration of biometric recognition systems • Adaptation and application of the EN 301 549 to biometric recognition systems.El reconocimiento biométrico, que es una tecnología ya madura, crece hoy en día en varios contextos, incluyendo la medicina forense, controles de acceso, sistemas de automatización del hogar, internet, etc. Ahora que la tecnología se está moviendo a los escenarios móviles, el reconocimiento biométrico está siendo también integrado en los teléfonos inteligentes, tabletas y otros dispositivos móviles como una solución conveniente para garantizar la seguridad, como complemento de otros métodos de seguridad como el PIN o las contraseñas. Sin embargo, el uso del reconocimiento biométrico es todavía desconocido para un amplio porcentaje de la población. Se ha demostrado [1] que algunas de las posibles razones de la lenta penetración de la biometría podrían estar relacionadas con problemas de usabilidad. Esto podría dar lugar a diversos inconvenientes, ofreciendo un rendimiento por debajo de lo esperado debido al mal uso de los sistemas y podría terminar con los usuarios rechazando la tecnología y prefiriendo otros enfoques. Esta tesis doctoral trata este tema incluyendo un estudio del estado actual de la técnica, varios experimentos que analizan los factores de usabilidad más relevantes y modificaciones a una metodología de evaluación de la usabilidad, la "H-B interaction" [2] basada en la ISO / IEC 19795 [3], el HBSI [4], la ISO 9241 [5] y Common Criteria [6]. Además, este trabajo se centra también en los problemas de accesibilidad de los sistemas de reconocimiento biométrico. Este tema, que por lo general se incluye en el campo de la usabilidad, se ha tratado aquí por separado, aunque el estudio de la accesibilidad ha seguido los mismos pasos que el estudio de usabilidad: revisión del estado del arte, análisis de los principales factores influyentes y propuesta de cambios en la metodología H-B interaction. Han sido también analizados los requisitos de accesibilidad para las Tecnologías de la Información y la Comunicación (TIC) en Europa, bajo la norma EN 301 549 [7]. Estos dos temas han sido estudiados a través de un enfoque centrado en el usuario (User Centric Design - UCD). De esta manera, se han detectado los factores influyentes. A continuación, dichos factores han sido aislados (cuando ha sido posible) y medidos. Los resultados obtenidos han sido interpretados para sugerir nuevos cambios a la metodología H-B interaction. Este enfoque de 3 pasos se ha aplicado de forma cíclica a los factores y a la metodología después de cada iteración. Debido a las tendencias tecnológicas y de usabilidad, durante este trabajo, todos los sistemas / aplicaciones desarrolladas en los experimentos se han pensado para ser móviles, directa o indirectamente. Las modalidades utilizadas durante los experimentos realizados en esta tesis doctoral son las que se señalaron como adecuados para el reconocimiento biométrico en dispositivos móviles: la firma manuscrita, la cara y el reconocimiento de huellas dactilares. Además, los escenarios y las aplicaciones utilizadas están en línea con los principales usos de la biometría en entornos móviles, como la firma de documentos, el bloqueo / desbloqueo de dispositivos, o hacer pagos. Los resultados de esta tesis tienen como objetivo orientar a los futuros desarrolladores en el diseño y evaluación de la usabilidad y la accesibilidad en los sistemas de reconocimiento biométrico. Por último, los resultados de esta tesis doctoral se sugerirán como un nuevo estándar de ISO / IEC / JTC1 / SC37 - Biometric Recognition, ya que la normalización es la manera adecuada de garantizar la usabilidad y la accesibilidad en los futuros sistemas biométricos. Las contribuciones de esta tesis incluyen: • Mejora de la metodología de evaluación H-B interaction, incluyendo varias evaluaciones de usabilidad. • Mejora de la accesibilidad de los sistemas de información / electrónicos mediante la integración de sistemas biométricos y varias evaluaciones. • Adaptación y aplicación de la norma de accesibilidad EN 301 549 al campo de los sistemas biométricos.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Patrizio Campisi.- Secretario: Enrique Cabellos Pardo.- Vocal: Marcos Faundez Zanu

Small UAS Detect and Avoid Requirements Necessary for Limited Beyond Visual Line of Sight (BVLOS) Operations

Potential small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) operational scenarios/use cases and Detect And Avoid (DAA) approaches were collected through a number of industry wide data calls. Every 333 Exemption holder was solicited for this same information. Summary information from more than 5,000 exemption holders is documented, and the information received had varied level of detail but has given relevant experiential information to generalize use cases. A plan was developed and testing completed to assess Radio Line Of Sight (RLOS), a potential key limiting factors for safe BVLOS ops. Details of the equipment used, flight test area, test payload, and fixtures for testing at different altitudes is presented and the resulting comparison of a simplified mathematical model, an online modeling tool, and flight data are provided. An Operational Framework that defines the environment, conditions, constraints, and limitations under which the recommended requirements will enable sUAS operations BVLOS is presented. The framework includes strategies that can build upon Federal Aviation Administration (FAA) and industry actions that should result in an increase in BVLOS flights in the near term. Evaluating approaches to sUAS DAA was accomplished through five subtasks: literature review of pilot and ground observer see and avoid performance, survey of DAA criteria and recommended baseline performance, survey of existing/developing DAA technologies and performance, assessment of risks of selected DAA approaches, and flight testing. Pilot and ground observer see and avoid performance were evaluated through a literature review. Development of DAA criteria—the emphasis here being well clear— was accomplished through working with the Science And Research Panel (SARP) and through simulations of manned and unmanned aircraft interactions. Information regarding sUAS DAA approaches was collected through a literature review, requests for information, and direct interactions. These were analyzed through delineation of system type and definition of metrics and metric values. Risks associated with sUAS DAA systems were assessed by focusing on the Safety Risk Management (SRM) pillar of the SMS (Safety Management System) process. This effort (1) identified hazards related to the operation of sUAS in BVLOS, (2) offered a preliminary risk assessment considering existing controls, and (3) recommended additional controls and mitigations to further reduce risk to the lowest practical level. Finally, flight tests were conducted to collect preliminary data regarding well clear and DAA system hazards