Search Results for author: Javier Hernandez-Ortega

Found 10 papers, 6 papers with code

FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment

1 code implementation3 Jan 2022 Javier Hernandez-Ortega, Julian Fierrez, Ignacio Serna, Aythami Morales

This comparison shows that, even though FaceQgen does not surpass the best existing face quality assessment methods in terms of face recognition accuracy prediction, it achieves good enough results to demonstrate the potential of semi-supervised learning approaches for quality estimation (in particular, data-driven learning based on a single high quality image per subject), having the capacity to improve its performance in the future with adequate refinement of the model and the significant advantage over competing methods of not needing quality labels for its development.

Face Recognition Image Quality Assessment +2

Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances

no code implementations23 Nov 2021 Javier Hernandez-Ortega, Julian Fierrez, Aythami Morales, Javier Galbally

The main scope of this chapter is to serve as an introduction to face presentation attack detection, including key resources and advances in the field in the last few years.

Face Presentation Attack Detection Face Recognition

FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliance

no code implementations3 Nov 2021 Javier Hernandez-Ortega, Julian Fierrez, Luis F. Gomez, Aythami Morales, Jose Luis Gonzalez-de-Suso, Francisco Zamora-Martinez

In this paper we develop FaceQvec, a software component for estimating the conformity of facial images with each of the points contemplated in the ISO/IEC 19794-5, a quality standard that defines general quality guidelines for face images that would make them acceptable or unacceptable for use in official documents such as passports or ID cards.

Face Recognition

Biometric Quality: Review and Application to Face Recognition with FaceQnet

1 code implementation5 Jun 2020 Javier Hernandez-Ortega, Javier Galbally, Julian Fierrez, Laurent Beslay

After a gentle introduction to the general topic of biometric quality and a review of past efforts in face quality metrics, in the present work, we address the need for better face quality metrics by developing FaceQnet.

Face Recognition

A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

no code implementations22 May 2020 Javier Hernandez-Ortega, Julian Fierrez, Aythami Morales, David Diaz

This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i. e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment.

Heart rate estimation

edBB: Biometrics and Behavior for Assessing Remote Education

1 code implementation10 Dec 2019 Javier Hernandez-Ortega, Roberto Daza, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia

We present a platform for student monitoring in remote education consisting of a collection of sensors and software that capture biometric and behavioral data.


Quality-based Pulse Estimation from NIR Face Video with Application to Driver Monitoring

no code implementations16 May 2019 Javier Hernandez-Ortega, Shigenori Nagae, Julian Fierrez, Aythami Morales

In this paper we develop a robust for heart rate (HR) estimation method using face video for challenging scenarios with high variability sources such as head movement, illumination changes, vibration, blur, etc.

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

3 code implementations3 Apr 2019 Javier Hernandez-Ortega, Javier Galbally, Julian Fierrez, Rudolf Haraksim, Laurent Beslay

Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development.

Face Recognition

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