Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation

Biometrics is the science of identifying an individual based on their intrinsic anatomical or behavioural characteristics, such as fingerprints, face, iris, gait, and voice. Iris recognition is one of the most successful methods because it exploits the rich texture of the human iris, which is unique even for twins and does not degrade with age. Modern approaches to iris recognition utilize deep learning to segment the valid portion of the iris from the rest of the eye, so it can then be encoded, stored and compared. This paper aims to improve the accuracy of iris semantic segmentation systems by introducing a novel data augmentation technique. Our method can transform an iris image with a certain dilation level into any desired dilation level, thus augmenting the variability and number of training examples from a small dataset. The proposed method is fast and does not require training. The results indicate that our data augmentation method can improve segmentation accuracy up to 15% for images with high pupil dilation, which creates a more reliable iris recognition pipeline, even under extreme dilation.Comment: 6 pages, 7 figures, 2 table

Object Detection and Classification in the Visible and Infrared Spectrums

The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark detection is presented. The third scenario addresses near-infrared cross-spectral periocular recognition with a coupled conditional generative adversarial network guided by auxiliary synthetic loss functions. Finally, a deep sparse feature selection and fusion is proposed to detect the presence of textured contact lenses prior to near-infrared iris recognition

Generative Adversarial Network and Its Application in Aerial Vehicle Detection and Biometric Identification System

In recent years, generative adversarial networks (GANs) have shown great potential in advancing the state-of-the-art in many areas of computer vision, most notably in image synthesis and manipulation tasks. GAN is a generative model which simultaneously trains a generator and a discriminator in an adversarial manner to produce real-looking synthetic data by capturing the underlying data distribution. Due to its powerful ability to generate high-quality and visually pleasingresults, we apply it to super-resolution and image-to-image translation techniques to address vehicle detection in low-resolution aerial images and cross-spectral cross-resolution iris recognition. First, we develop a Multi-scale GAN (MsGAN) with multiple intermediate outputs, which progressively learns the details and features of the high-resolution aerial images at different scales. Then the upscaled super-resolved aerial images are fed to a You Only Look Once-version 3 (YOLO-v3) object detector and the detection loss is jointly optimized along with a super-resolution loss to emphasize target vehicles sensitive to the super-resolution process. There is another problem that remains unsolved when detection takes place at night or in a dark environment, which requires an IR detector. Training such a detector needs a lot of infrared (IR) images. To address these challenges, we develop a GAN-based joint cross-modal super-resolution framework where low-resolution (LR) IR images are translated and super-resolved to high-resolution (HR) visible (VIS) images before applying detection. This approach significantly improves the accuracy of aerial vehicle detection by leveraging the benefits of super-resolution techniques in a cross-modal domain. Second, to increase the performance and reliability of deep learning-based biometric identification systems, we focus on developing conditional GAN (cGAN) based cross-spectral cross-resolution iris recognition and offer two different frameworks. The first approach trains a cGAN to jointly translate and super-resolve LR near-infrared (NIR) iris images to HR VIS iris images to perform cross-spectral cross-resolution iris matching to the same resolution and within the same spectrum. In the second approach, we design a coupled GAN (cpGAN) architecture to project both VIS and NIR iris images into a low-dimensional embedding domain. The goal of this architecture is to ensure maximum pairwise similarity between the feature vectors from the two iris modalities of the same subject. We have also proposed a pose attention-guided coupled profile-to-frontal face recognition network to learn discriminative and pose-invariant features in an embedding subspace. To show that the feature vectors learned by this deep subspace can be used for other tasks beyond recognition, we implement a GAN architecture which is able to reconstruct a frontal face from its corresponding profile face. This capability can be used in various face analysis tasks, such as emotion detection and expression tracking, where having a frontal face image can improve accuracy and reliability. Overall, our research works have shown its efficacy by achieving new state-of-the-art results through extensive experiments on publicly available datasets reported in the literature

Cyclic Style Generative Adversarial Network for Near Infrared and Visible Light Face Recognition

Face recognition in the visible light (VIS) spectrum has been widely utilized in many practical applications. With the development of the deep learning method, the recognition accuracy and speed have already reached an excellent level, where face recognition can be applied in various circumstances. However, in some extreme situations, there are still problems that face recognition cannot guarantee performance. One of the most significant cases is under poor illumination. Lacking light sources, images cannot show the true identities of detected people. To address such a problem, the near infrared (NIR) spectrum offers an alternative solution to face recognition in which face images can be captured clearly. Studies have been made in recent years, and current near infrared and visible light (NIR-VIS) face recognition methods have achieved great performance. In this thesis, I review current NIR-VIS face recognition methods and public NIR-VIS face datasets. I first list public NIR-VIS face datasets that are used in most research. For each dataset, I represent their characteristics, including the number of subjects, collection environment, resolution of images, and whether paired or not. Also, I conclude evaluation protocols for each dataset, helping with further analyzing of performances. Then, I classify current NIR-VIS face recognition methods into three categories, image synthesis-based methods, subspace learning-based methods, and invariant feature-based methods. The contribution of each method is concisely explained. Additionally, I make comparisons between current NIR-VIS face recognition methods and propose my own opinion on the advantages and disadvantages of these methods. To improve the shortcomings of current methods, this thesis proposes a new model, Cyclic Style Generative Adversarial Network (CS-GAN), which is a combination of image synthesis-based method and subspace learning-based method. The proposed CS-GAN improves the visualization results of image synthesis between the NIR domain and VIS domain as well as recognition accuracy. The CS-GAN is based on the Style-GAN 3 network which was proposed in 2021. In the proposed model, there are two generators from pre-trained Style-GAN 3 which generate images in the NIR domain and VIS domain, respectively. The generators consist of a mapping network and synthesis network, where the mapping network disentangles the latent code for reducing correlation between features, and the synthesis network synthesizes face images through progressive growing training. The generators have different final layers, a to-RGB layer for the VIS domain and a to-grayscale layer for the NIR domain. Generators are embedded in a cyclic structure, in which latent codes are sent into the synthesis network in the other generator for recreated images, and recreated images are compared with real images which in the same domain to ensure domain consistency. Besides, I apply the proposed cyclic subspace learning. The cyclic subspace learning is composed of two parts. The first part introduces the proposed latent loss which is to have better controls over the learning of latent subspace. The latent codes influence both details and locations of features through continuously inputting into the synthesis network. The control over latent subspace can strengthen the feature consistency between synthesized images. And the second part improves the style-transferring process by controlling high-level features with perceptual loss in each domain. In the perceptual loss, there is a pre-trained VGG-16 network to extract high-level features which can be regarded as the style of the images. Therefore, style loss can control the style of images in both domains as well as ensure style consistency between synthesized images and real images. The visualization results show that the proposed CS-GAN model can synthesize better VIS images that are detailed, corrected colorized, and with clear edges. More importantly, the experimental results show that the Rank-1 accuracy on CASISA NIR-VIS 2.0 database reaches 99.60% which improves state-of-the-art methods by 0.2%

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area