Zeit: 18.02.2019, 15:00 Uhr
Ort: Raum 072 im Fraunhofer IGD, Fraunhoferstrasse 5, S3|05
Referent: Fadi Boutros (Betreuer: Naser Damer)
Titel: "Reducing Ethnic Bias of Faces Recognition by Ethnic Augmentation" (Masterarbeit)
Abstract: Automated face recognition has gained wider deployment ground after the recent accuracy gains achieved by deep learning techniques. Despite the rapid performance gains, face recognition still suffers from very critical issues. One of the recently uncovered, and very sensitive, challenges is the ethnicity bias in face recognition decision. This is the case, unfortunately, even in the latest commercial and academic face recognition system. In 2018, the National Institute of Standards and Technology (NIST) published the latest report regarding the evaluation result of commercial face recognition solutions from several major biometric vendors. The report specifically evaluated and demonstrated the variance of the error rates of the evaluated solutions based on demographic variations.
This thesis is one of the first research efforts in addressing the decision bias challenge in biometrics. It builds its hypothesis on the strong assumption that ethnicity bias is caused by the relative under-representation of certain ethnicities in training databases. This work introduces a novel ethnicity-driven data augmentation approach to reduce biometric bias. The proposed approach successfully utilize a generative image model to generate new face images that preserve the identity of their source images while partially transforming their ethnicity to the targeted ethnicity group. A large-scale ethnicity-labeled face images database is created in order to develop and evaluate the proposed approach. To achieve that, part of this thesis focused on creating an ethnicity classifier to annotate face images, achieving accuracy in the state-of-the-art range. The proposed ethnicity-driven face generative model is developed based on the ethnicity labeled images to generate realistically and high-resolution face images, depending on a limit amount of training data. More importantly, the thesis proves that the proposed augmentation approach strongly preserves the identity of the input images and partially transforms the ethnicity.
The augmented images are used as part of the training data of a face recognition model. The achieved verification results prove that the proposed ethnicity augmentation methods significantly and consistently reduced the ethnicity bias of the face recognition model. For examples, the ERR was reduced from 0.159 to 0.130 when verifying inter-ethnicity samples of Black individuals on a model trained on Asian individual images, respectively before and after applying the proposed training data augmentation. Moreover, the overall performance of the face recognition model was improved. However, this improvement was more significant, as intended, in the targeted ethnicity groups.


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