DANTCHEVA , Antitza

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Manipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing , such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and...

Automated gender estimation has numerous applications including video surveillance, human computer-interaction, anonymous customized advertisement and image retrieval. Most commonly, the underlying algorithms analyze the facial appearance for clues of gender. In this work we propose a novel method for gender estimation, which exploits dynamic...

Expression recognition remains challenging, predominantly due to (a) lack of sufficient data, (b) subtle emotion intensity, (c) subjective and inconsistent annotation, as well as due to (d) in-the-wild data containing variations in pose, intensity, and occlusion. To address such challenges in a unified framework, we propose a self-training...

Facial beautification induced by plastic surgery, cosmetics or retouching has the ability to substantially alter the appearance of face images. Such types of beautification can negatively affect the accuracy of face recognition systems. In this work, a conceptual categorisation of beautification is presented, relevant scenarios with respect to...

Automated gender estimation has numerous applications including video surveillance, human computer-interaction, anonymous customized advertisement and image retrieval. Most commonly, the underlying algorithms analyze facial appearance for clues of gender. In this work, we propose a novel approach for gender estimation, based on facial behavior...

Facial attributes are instrumental in semantically characterizing faces. Automated classification of such attributes (i.e., age, gender, ethnicity) has been a well studied topic. We here seek to explore the inverse problem, namely given attribute-labels the generation of attribute-associated faces. The interest in this topic is fueled by...

This work explores joint classification of gender, age and race. Specifically, we here propose a Multi-Task Convolution Neural Network (MTCNN) employing joint dynamic loss weight adjustment towards classification of named soft biometrics, as well as towards mitigation of soft biometrics related bias. The proposed algorithm achieves promising...

Body height, weight, as well as the associated and composite body mass index (BMI) are human attributes of pertinence due to their use in a number of applications including surveillance, re-identification, image retrieval systems, as well as healthcare. Previous work on automated estimation of height, weight and BMI has predominantly focused on...

Recent advances in computer vision have aimed at extracting and classifying auxiliary biometric information such as age, gender, as well as health attributes, referred to as soft biometrics or attributes. We here seek to explore the inverse problem, namely face generation based on attribute labels, which is of interest due to related...

Recent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have...

Manipulated images and videos, i.e., deepfakes have become increasingly realistic due to the tremendous progress of deep learning methods. However, such manipulation has triggered social concerns, necessitating the introduction of robust and reliable methods for deepfake detection. In this work, we explore a set of attention mechanisms and...

Fig. 1. Qualitative results of HiFaceGAN, SRGAN, Pix2Pix, AxialGAN and the proposed ANYRES on the ARL-VTF dataset. We decrease resolution in each row (re-scaled to 128×128). While previous methods are impaired to super resolve facial images for a given resolution by using one specific network for each resolution, our proposed ANYRES achieves a...

Recent research has demonstrated the negative impact of makeup on automated face recognition. In this work, we introduce a patch-based ensemble learning method, which uses multiple subspaces generated by sampling patches from before-makeup and after-makeup face images, to address this problem. In the proposed scheme, each face image is...

The universal hypothesis suggests that the six basic emotions-anger, disgust, fear, happiness, sadness, and surprise-are being expressed by similar facial expressions by all humans. While existing datasets support the universal hypothesis and comprise of images and videos with discrete disjoint labels of profound emotions, real-life data...

In this paper, we propose a manifold based facial expression recognition framework which utilizes the intrinsic structure of the data distribution to accurately classify the expression categories. Specifically, we model the expressive faces as the points on linear subspaces embedded in a Grassmannian manifold, also called as expression...

Facial expression recognition aims to accurately interpret facial muscle movements in affective states (emotions). Previous studies have proposed holistic analysis of the face, as well as the extraction of features pertained only to specific facial regions towards expression recognition. While classically the latter have shown better...

Soft biometrics traits are physical, behavioral, or material accessories associated with an individual, which can be used for describing an individual. These attributes are typically gleaned from primary biometric data, are classifiable in pre-defined human understandable categories, and can be extracted in an automated manner. Examples include...

Generating human videos based on single images entails the challenging simultaneous generation of realistic and visual appealing appearance and motion. In this context, we propose a novel conditional GAN architecture, namely ImaGINator, which given a single image, a condition (la-bel of a facial expression or action) and noise, decomposes...

Creating realistic human videos entails the challenge of being able to simultaneously generate both appearance, as well as motion. To tackle this challenge, we introduce G 3 AN, a novel spatio-temporal generative model, which seeks to capture the distribution of high dimensional video data and to model appearance and motion in disentangled...

Thermal to visible face image translation aims at synthesizing high-fidelity visible face images from thermal counterparts, placing emphasis on preserving the identity of the faces. While remarkable progress has been achieved related to the quality of synthetic images, as well as related to associated face matching accuracy, interpreting the...

Manipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and...

Face recognition has been widely accepted as a means of identification in applications ranging from border control to security in the banking sector. Surprisingly, while widely accepted, we still lack the understanding of uniqueness or distinctiveness of faces as biometric modality. In this work, we study the impact of factors such as image...

Due to the remarkable progress of deep generative models, animating images has become increasingly efficient, whereas associated results have become increasingly realistic. Current animation-approaches commonly exploit structure representation extracted from driving videos. Such structure representation is instrumental in transferring motion...