Search Results for author: Enrique Sánchez-Lozano

Found 5 papers, 0 papers with code

Improving memory banks for unsupervised learning with large mini-batch, consistency and hard negative mining

no code implementations8 Feb 2021 Adrian Bulat, Enrique Sánchez-Lozano, Georgios Tzimiropoulos

An important component of unsupervised learning by instance-based discrimination is a memory bank for storing a feature representation for each training sample in the dataset.

Inferring Dynamic Representations of Facial Actions from a Still Image

no code implementations4 Apr 2019 Siyang Song, Enrique Sánchez-Lozano, Linlin Shen, Alan Johnston, Michel Valstar

We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation.

FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge

no code implementations14 Feb 2017 Michel F. Valstar, Enrique Sánchez-Lozano, Jeffrey F. Cohn, László A. Jeni, Jeffrey M. Girard, Zheng Zhang, Lijun Yin, Maja Pantic

The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views.

Benchmarking Facial Action Unit Detection +4

A Functional Regression approach to Facial Landmark Tracking

no code implementations7 Dec 2016 Enrique Sánchez-Lozano, Georgios Tzimiropoulos, Brais Martinez, Fernando de la Torre, Michel Valstar

This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker.

Face Detection Incremental Learning +2

Cascaded Continuous Regression for Real-time Incremental Face Tracking

no code implementations3 Aug 2016 Enrique Sánchez-Lozano, Brais Martinez, Georgios Tzimiropoulos, Michel Valstar

We then derive the incremental learning updates for CCR (iCCR) and show that it is an order of magnitude faster than standard incremental learning for cascaded regression, bringing the time required for the update from seconds down to a fraction of a second, thus enabling real-time tracking.

Face Alignment Incremental Learning +2

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