Luis S. Luévano, PhD

Luis S. Luévano, PhD

Assistant Professor at the Autonomous University of Barcelona (UAB). My current research interests are Face Recognition, Face Anti-Spoofing, Privacy, and Decentralized Machine Learning.

About me

Researcher focused in Computer Vision, Biometrics, Privacy, and Decentralized Machine Learning. I am currently working as an Assitant Professor at the Autonomous University of Barcelona (UAB) in Barcelona, Spain. Previously, I worked as a Postdoctoral Researcher at the Biometrics Security & Privacy group at Idiap in Switzerland and at the WIDE Team at Inria in the University of Rennes in France. My current research interests are Face Recognition, Face Anti-Spoofing, Privacy, and Decentralized Machine Learning. My PhD thesis work was on Binarized Neural Networks for Very Low Resolution Face Recognition for deployment on embedded devices.

🔬 Latest research

Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap

Published in IEEE International Joint Conference on Biometrics (IJCB) 2026, Focus Session on Generative AI for Fair and Secure Biometrics under Limited Data, 2026

Face Recognition (FR) systems in surveillance settings often encounter Low Resolution (LR) faces, those whose face region falls below the standard 112 × 112 input size. While labelled High Resolution (HR) training data is abundant, labelled native-LR data, and above all paired native-LR/HR data, is scarce. One workaround is to synthesize LR data from the available HR faces, but how much synthesis effort is repaid in recognition accuracy remains unclear. We present a study of simple synthetic generation strategies for a compact, edge device-oriented face recognition system, spanning interpolation-based degradation, knowledge distillation, a Prepended Domain Transformer (PDT), Real-ESRGAN-style degradation, and a learned Super Resolution (SR) front-end with an identity-aware loss. We evaluate these strategies on synthetic cross-resolution face benchmarks (LFW, CFP-FP, AgeDB-30) and on TinyFace, a real-world native LR dataset, and expose a synthetic–real gap: the degradation setting that is optimal on synthetic benchmarks is not the one that is optimal on real LR. We find that more synthesis effort does not help monotonically: the learned SR front-end does not surpass a direct feed of the aligned LR image into a strong backbone, while simple interpolation augmentation of a compact backbone is the only synthesis that improves over its own baseline. We conclude that generative methods for LR face recognition must be validated on real LR and against a direct-feed baseline, and release our pipeline at https://idiap.ch/paper/synth-lrfr.

Recommended citation: Luis S. Luevano, Ünsal Öztürk, Hatef Otroshi Shahreza, Anjith George, Sébastien Marcel. "Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap". IEEE International Joint Conference on Biometrics (IJCB) 2026, Focus Session on Generative AI for Fair and Secure Biometrics under Limited Data.

GaitFace: A Multimodal Dataset for Long-Range Person Identification

Published in IEEE International Joint Conference on Biometrics (IJCB) 2026, 2026

Efficient border control is becoming a significant global challenge, mainly due to severe congestion and extended passenger waiting times. To mitigate these bottlenecks and facilitate passenger flow, biometric technologies are increasingly deployed to streamline identity verification and enhance crossing efficiency. Technical limitations frequently impede biometric identification, particularly in long-range surveillance, where systems must deal with adverse atmospheric conditions and degraded image quality. While high-quality frameworks like BRIAR exist, they are frequently restricted to specific government agencies. This paper introduces GaitFace, a new public dataset that contains face and gait data captured at long distances. To ensure that the research reflects authentic border scenarios, we use Pre-Enrollment data, where a traveler registers via a mobile device, and “In-the-Wild” captures, which records individuals at a distance across multiple viewing angles and different cameras. Benchmarking SOTA face and gait models reveals that current architectures fail under low-resolution and elevated viewpoints despite success with optical assistance. GaitFace exposes these critical vulnerabilities, providing a rigorous public benchmark to drive more robust, unconstrained biometric research.

Recommended citation: Alain Komaty, Luis S. Luevano, Vidit Vidit, Anjith George, Zeina Al Amine, Sébastien Marcel. "GaitFace: A Multimodal Dataset for Long-Range Person Identification". IEEE International Joint Conference on Biometrics (IJCB) 2026. https://www.idiap.ch/paper/gaitface/

📰 News: