activity
20192026
most citedHuman-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment

54 citations · 83 across the 8 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.CV2020

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★ 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

SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learning

Ignacio Serna, Aythami Morales, Julian Fierrez +3

We propose a discrimination-aware learning method to improve both accuracy and fairness of biased face recognition algorithms. The most popular face recognition benchmarks assume a…

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…