REPORT Dayer_Idiap-Com-04-2020/IDIAP Face Recognition systems: performance evaluation and bias analysis Dayer, Yannick Artificial intelligence bias Convolutional neural network Face Recognition neural network EXTERNAL http://publications.idiap.ch/attachments/reports/2020/Dayer_Idiap-Com-04-2020.pdf PUBLIC Idiap-Com-04-2020 2020 Idiap Rue Marconi 19, 1920 Martigny August 2020 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.