T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models

Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using visible spectrum images. But, acquiring images in the visible spectrum is impractical in scenarios of low-light and nighttime conditions, and often images are captured in an alternate domain such as the thermal infrared domain. Facial verification in thermal images is often performed after retrieving the corresponding visible domain images. This is a well-established problem often known as the Thermal-to-Visible (T2V) image translation. In this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based solution for T2V translation specifically for facial images. During training, the model learns the conditional distribution of visible facial images given their corresponding thermal image through the diffusion process. During inference, the visible domain image is obtained by starting from Gaussian noise and performing denoising repeatedly. The existing inference process for DDPMs is stochastic and time-consuming. Hence, we propose a novel inference strategy for speeding up the inference time of DDPMs, specifically for the problem of T2V image translation. We achieve the state-of-the-art results on multiple datasets. The code and pretrained models are publically available at http://github.com/Nithin-GK/T2V-DDPMComment: Accepted at The IEEE conference series on Automatic Face and Gesture Recognition 202

Emotional Prosody Processing in the Schizophrenia Spectrum.

THESIS ABSTRACT Emotional prosody processing impairment is proposed to be a main contributing factor for the formation of auditory verbal hallucinations in patients with schizophrenia. In order to evaluate such assumption, five experiments in healthy, highly schizotypal and schizophrenia populations are presented. The first part of the thesis seeks to reveal the neural underpinnings of emotional prosody comprehension (EPC) in a non-clinical population as well as the modulation of prosodic abilities by hallucination traits. By revealing the brain representation of EPC, an overlap at the neural level between EPC and auditory verbal hallucinations (AVH) was strongly suggested. By assessing the influence of hallucinatory traits on EPC abilities, a continuum in the schizophrenia spectrum in which high schizotypal population mirrors the neurocognitive profile of schizophrenia patients was established. Moreover, by studying the relation between AVH and EPC in non-clinical population, potential confounding effects of medication influencing the findings were minimized. The second part of the thesis assessed two EPC related abilities in schizophrenia patients with and without hallucinations. Firstly, voice identity recognition, a skill which relies on the analysis of some of the same acoustical features as EPC, has been evaluated in patients and controls. Finally, the last study presented in the current thesis, assessed the influence that implicit processing of emotional prosody has on selective attention in patients and controls. Both patients studies demonstrate that voice identity recognition deficits as well as abnormal modulation of selective attention by implicit emotion prosody are related to hallucinations exclusively and not to schizophrenia in general. In the final discussion, a model in which EPC deficits are a crucial factor in the formation of AVH is evaluated. Experimental findings presented in the previous chapters strongly suggests that the perception of prosodic features is impaired in patients with AVH, resulting in aberrant perception of irrelevant auditory objects with emotional prosody salience which captures the attention of the hearer and which sources (speaker identity) cannot be recognized. Such impairments may be due to structural and functional abnormalities in a network which comprises the superior temporal gyrus as a central element

A study of deep learning and its applications to face recognition techniques

El siguiente trabajo es el resultado de la tesis de maestría de Fernando Suzacq. La tesis se centró alrededor de la investigación sobre el reconocimiento facial en 3D, sin la reconstrucción de la profundidad ni la utilización de modelos 3D genéricos. Esta investigación resultó en la escritura de un paper y su posterior publicación en IEEE Transactions on Pattern Analysis and Machine Intelligence. Mediante el uso de iluminación activa, se mejora el reconocimiento facial en 2D y se lo hace más robusto a condiciones de baja iluminación o ataques de falsificación de identidad. La idea central del trabajo es la proyección de un patrón de luz de alta frecuencia sobre la cara de prueba. De la captura de esta imagen, nos es posible recuperar información real 3D, que se desprende de las deformaciones de este patrón, junto con una imagen 2D de la cara de prueba. Este proceso evita tener que lidiar con la difícil tarea de reconstrucción 3D. En el trabajo se presenta la teoría que fundamenta este proceso, se explica su construcción y se proveen los resultados de distintos experimentos realizados que sostienen su validez y utilidad. Para el desarrollo de esta investigación, fue necesario el estudio de la teoría existente y una revisión del estado del arte en este problema particular. Parte del resultado de este trabajo se presenta también en este documento, como marco teórico sobre la publicación

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%

A Survey of Face Recognition

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023