Selected Publications

Research in face perception and emotion theory requires very large annotated databases of images of facial expressions of emotion. Annotations should include Action Units (AUs) and their intensities as well as emotion category. This goal cannot be readily achieved manually. Herein, we present a novel computer vision algorithm to annotate a large database of one million images of facial expressions of emotion in the wild (i.e., face images downloaded from the Internet). First, we show that this newly proposed algorithm can recognize AUs and their intensities reliably across databases. To our knowledge, this is the first published algorithm to achieve highly-accurate results in the recognition of AUs and their intensities across multiple databases. Our algorithm also runs in real-time (>30 images/second), allowing it to work with large numbers of images and video sequences. Second, we use WordNet to download 1,000,000 images of facial expressions with associated emotion keywords from the Internet. These images are then automatically annotated with AUs, AU intensities and emotion categories by our algorithm. The result is a highly useful database that can be readily queried using semantic descriptions for applications in computer vision, affective computing, social and cognitive psychology and neuroscience; e.g., “show me all the images with happy faces” or “all images with AU 1 at intensity c.”
In CVPR 2016

Recent Publications

  • Fast and Precise Face Alignment and 3D Shape Reconstruction from a Single 2D Image

    Details PDF

  • Recognition of Action Units in the Wild with Deep Nets and a New Global-Local Loss


  • EmotioNet Challenge: Recognition of facial expressions of emotion in the wild

    Details PDF Dataset

  • The not face: A grammaticalization of facial expressions of emotion

    Details PDF

  • EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild

    Details PDF Video Dataset

  • Multiobjective Optimization for Model Selection in Kernel Methods in Regression

    Details PDF Code

  • Discriminant Features and Temporal Structure of Nonmanuals in American Sign Language

    Details PDF Code Dataset

  • Salient and non-salient fiducial detection using a probabilistic graphical model

    Details PDF Code

Recent Posts

Our group is glad of having our paper “Recognition of Action Units in the Wild with Deep Nets and a New Global-Local” accepted to be presented in ICCV 2017.



CBCSL Projects

My group deals with a large range of topics from neuroscience, passing by cognitive science to machine learning and AI

Other things

Rarely I write not-academic things, mostly in spanish in a separate blog, just in case that you want to take a look


I’ve been either a lecturer or an assistant for the Computer Vision class at the Ohio State University.

  • ECE5460: Image Processing (but it’s really computer vision)