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INDUSTRIAL PROPERTY STATUS:

Spanish Patent Application (P201831278), expandable to international protection, 100% Autonoma University of Madrid (UAM).

TYPE OF COLLABORATION BEING SHOUGHT:

Licensing agreement.
R&D development agreement.

For further information we recommend:

Scientific publications:

A. Morales, J. Fierrez, R. Vera-Rodriguez, R. Tolosana. SensitiveNets: Learning Agnostic Representations with Application to Face Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. [pdf][GitHub]
I. Serna, A. Morales, J. Fierrez, M. Cebrian, N. Obradovich and I. Rahwan. Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics. Proc. of AAAI Workshop on Artificial Intelligence Safety (SafeAI), New York, NY, USA, February 2020. [pdf]
I. Serna, A. Morales, J. Fierrez, M. Cebrian, N. Obradovich, I. Rahwan, “SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learning,” arXiv:2004.11246, 2020. [pdf]
A. Morales, J. Fierrez, R. Vera-Rodriguez, “SensitiveNets: Unlearning Undesired Information for Generating Agnostic Representations with Application to Face Recognition”, in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Workshop on Fairness Accountability Transparency and Ethics in Computer Vision (CVPR FATE/CV), Long Beach, USA, 2019. [pdf]
R. Vera-Rodriguez, M. Blazquez, A. Morales, E. Gonzalez-Sosa, J. Neves and H. Proenca. FaceGenderID: Exploiting Gender Information in DCNNs Face Recognition Systems. Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Workshop on Bias Estimation in Face Analytics (CVPR BEFA), June 2019 (Best Paper Runner Up Award). [pdf]
A. Acien, A. Morales, R. Vera-Rodriguez, I. Bartolome, J. Fierrez. Measuring the Gender and Ethnicity Bias in Deep Models for Face Recognition. Proc. of IAPR Iberoamerican Congress on Pattern Recognition, Madrid, Spain, 2018. [pdf]
B. Goodman and F. Flaxman. European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 2016.
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, C. Dwork. Learning Fair Representations. In Proc. of the Int. Conf. on Machine Learning, Atlanta, USA, pp. 325-333, 2013.
J. Buolamwini and T. Gebru. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proc. of the ACM Conf. on Fairness, Accountability, and Transparency, New York, USA, 81:1-15, 2018.
M. Alvi, A. Zisserman, C. Nellaker. Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings. Proc. of European Conf. on Computer Vision, Munich, Germany, 2018.