%Aigaion2 BibTeX export from Idiap Publications %Friday 13 December 2024 05:13:48 PM @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} }