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 [BibTeX] [Marc21]
Face Recognition systems: performance evaluation and bias analysis
Type of publication: Idiap-Com
Citation: Dayer_Idiap-Com-04-2020
Number: Idiap-Com-04-2020
Year: 2020
Month: 8
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.
Keywords: Artificial intelligence, bias, Convolutional neural network, Face Recognition, neural network
Projects Idiap
Authors Dayer, Yannick
Added by: [ADM]
Total mark: 3
  • Dayer_Idiap-Com-04-2020.pdf (MD5: a72c12d31abf848d4ca8df7998c318c1)