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@TECHREPORT{Dayer_Idiap-Com-04-2020,
                      author = {Dayer, Yannick},
                    keywords = {Artificial intelligence, bias, Convolutional neural network, Face Recognition, neural network},
                    projects = {Idiap},
                       month = {8},
                       title = {Face Recognition systems: performance evaluation and bias analysis},
                        type = {Idiap-Com},
                      number = {Idiap-Com-04-2020},
                        year = {2020},
                 institution = {Idiap},
                     address = {Rue Marconi 19, 1920 Martigny},
                    abstract = {User authentication is a crucial part of data security, and biometrics is an advantageous way of achieving this. Face images capture being minimally invasive and easy to acquire makes face recognition a good contender for being used in a lot of applications that require to know if the user is really who he claims he is.
In this thesis, I compare the performance of multiple existing face recognition systems on different datasets.
I then present how a convolutional neural network system works, and show the performance results of such a system trained from scratch for face recognition. I show that training a big neural network with few images is detrimental, and a big training dataset is required.
An experiment on racial bias evaluation is then presented with methods to reduce the disparity between ethnicity in the products of a face recognition system.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2020/Dayer_Idiap-Com-04-2020.pdf}
}