A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

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. Four alternatives from the literature are tested, three based in hand crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand crafted ones. The low error rate achieved by the learning based model makes possible its application in real scenarios, e.g. in medical or sports environments.Comment: Accepted in "IEEE International Workshop on Medical Computing (MediComp) 2020

Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16181-5_54Proceedings in Computer Vision - ECCV 2014 Workshops held in Zurich (Switzerland) on 2015.This paper studies ways to detect good users for biometric recognition based on keystroke dynamics. Keystroke dynamics is an active research field for the biometric scientific community. Despite the great efforts made during the last decades, the performance of keystroke dynamics recognition systems is far from the performance achieved by traditional hard biometrics. This is very pronounced for some users, who generate many recognition errors even with the most sophisticate recognition algorithms. On the other hand, previous works have demonstrated that some other users behave particularly well even with the simplest recognition algorithms. Our purpose here is to study ways to distinguish such classes of users using only the genuine enrollment data. The experiments comprise a public database and two popular recognition algorithms. The results show the effectiveness of the Kullback-Leibler divergence as a quality measure to categorize users in comparison with other four statistical measures.This work has been partially supported by projects Bio-Shield (TEC2012-34881) from Spanish MINECO and BEAT (FP7-SEC-284989) from EU