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%

Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate