activity
20202026
most citedHuman-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment

54 citations · 86 across the 10 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.CV2020★ 5 cited

Facial Expressions as a Vulnerability in Face Recognition

Alejandro Peña, Ignacio Serna, Aythami Morales +2

This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition syste…

cs.CV2020

Learning Emotional-Blinded Face Representations

Alejandro Peña, Julian Fierrez, Agata Lapedriza +1

We propose two face representations that are blind to facial expressions associated to emotional responses. This work is in part motivated by new international regulations for pers…

cs.CV2020★ 3 cited

FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment

Alejandro Peña, Ignacio Serna, Aythami Morales +1

With the aim of studying how current multimodal AI algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, this de…

cs.CV2020

Bias in Multimodal AI: Testbed for Fair Automatic Recruitment

Alejandro Peña, Ignacio Serna, Aythami Morales +1

The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new s…

cs.CV2020

InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics

Ignacio Serna, Alejandro Peña, Aythami Morales +1

This work explores the biases in learning processes based on deep neural network architectures. We analyze how bias affects deep learning processes through a toy example using the…