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Face Recognition in Real-world Images

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Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Sébastien Marcel, Christophe René Joseph Ecabert, Parsa Rahimi Noshanagh

Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various facial attributes such as gender and origin. The main reason are the biases in ...

IEEE2023

A Novel Framework for Assessment of Deep Face Recognition Systems in Realistic Conditions

Touradj Ebrahimi, Yuhang Lu

Deep convolutional neural networks have shown remarkable results on face recognition (FR). Despite their significant progress, the performance of current face recognition techniques is often assessed in benchmarks under not always realistic conditions. The ...

2022

Are GAN-based Morphs Threatening Face Recognition?

Sébastien Marcel, Eklavya Sarkar

Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control ...

IEEE2022

Sparse Autoencoders for Speech Modeling and Recognition

Selen Hande Kabil

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

EPFL2023

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Sébastien Marcel, Hatef Otroshi Shahreza

State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system's database and are used for recognition of ...

IEEE2022

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Kai Jappe Sandbrink

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

eLIFE SCIENCES PUBL LTD2023

Regularization of polynomial networks for image recognition

Volkan Cevher, Grigorios Chrysos, Bohan Wang

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

2023

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Hervé Bourlard, Selen Hande Kabil

Starting from a strong Lattice-Free Maximum Mutual Information (LF-MMI) baseline system, we explore different autoencoder configurations to enhance Mel-Frequency Cepstral Coefficients (MFCC) features. Autoencoders are expected to generate new MFCC features ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

Petr Motlicek, Juan Pablo Zuluaga Gomez

In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI)-based tools. The virtual simulation-pilot engine receives spoken ...

MDPI2023

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Thomas Jean Debarre

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Natural-Looking Adversarial Examples From Freehand Sketches

Davide Nanni, Hakgu Kim

Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to misl ...

IEEE2022

Regularization of polynomial networks for image recognition

Volkan Cevher, Grigorios Chrysos, Bohan Wang

Ieee Computer Soc2023

The spectral bias of polynomial neural networks

Volkan Cevher, Grigorios Chrysos, Leello Tadesse Dadi

Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...

2022

Towards learning-based denoising of light fields

Touradj Ebrahimi, Michela Testolina

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

2023

Indexing Protected Deep Face Templates by Frequent Binary Patterns

Sébastien Marcel, Hatef Otroshi Shahreza

In this work, we present a simple biometric indexing scheme which is binning and retrieving cancelable deep face templates based on frequent binary patterns. The simplicity of the proposed approach makes it applicable to unprotected as well as protected, i ...

IEEE2022

Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Touradj Ebrahimi, Yuhang Lu

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage pri ...

2023

On combining denoising with learning-based image decoding

Touradj Ebrahimi, Michela Testolina

Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captu ...

SPIE2022

Transfer learning application of self-supervised learning in ARPES

Gabriel Aeppli

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

IOP Publishing Ltd2023

Improving Object Detection under Domain Shifts

Vidit Vidit

Object detection plays a critical role in various computer vision applications, encompassing domains like autonomous vehicles, object tracking, and scene understanding. These applica- tions rely on detectors that generate bounding boxes around known object ...

EPFL2023

A Two-Step Approach To Leverage Contextual Data: Speech Recognition In Air-Traffic Communications

Petr Motlicek, Juan Pablo Zuluaga Gomez

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead ...

IEEE2022