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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}
}