# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 2 Related Work
### Investigating Bias in Facial Data-Based Technology  
Georgopoulos et al. [11] tested different deep learning based-face technology models such as facial recognition and gender prediction to find whether there is a bias in those systems or not. Moreover, the ten coding schemes method was used in [12] to analyze the diversity of subjects' faces in their proposed KANface dataset. In term of gender, female images were better recognized than male ones. Whereas, videos samples for both genders were recognized with similar accuracy. Klare et al. [13] also investigated bias in face recognition and the effect of demographic distributions of dataset on the performance of face recognition models. In this research, six open source trainable and non-trainable and commercial non-deep CNN-based models were examined on the Pinellas County Sheriff's Office (PCSO) image dataset. The dataset was divided to eight groups for different genders, races, and age scales. The results demonstrated that youth, women, and dark skin groups were harder to recognize by all models. Receiver operating characteristic (ROC) curves and false match rate (FMR) were used to evaluate the matching accuracy.  
The fairness of face verification of different groups with one or more demographic attributes was studied by researchers in [14]. They used equally diverse subset of MORPH dataset to recognize and verify subjects from different gender, age, and race cohorts. Two models were used in the study: One was commercial and the second was based on the Oxford Visual Geometry Group's VGG network. They concluded that the women were harder to be verified than men, and demographic attributes have considerable effect on the verification accuracy. Robinson et al. [15] conducted many experiment to closely observe the effect of gender and ethnicity on face verification models. They found that face verification was more accurate for males than females, which concur with the results in [14].  
In addition, Cook et al. [16] studied different types of datasets, which are not publicly available. Cook et al. [16] used images of subjects from eleven different face acquisition systems to examine the effect of skin reflectance, age, eyewear, gender, and height on the performance of the acquisition systems. Findings showed that demographic attributes have an effect on the accuracy of the biometric system. Subjects with darker skin color, females, young people, and subjects without eyewear had less efficient and inaccurate image acquisition process.  
The fairness of gender prediction tools was studied by researchers in [17]. They used three COTs software to predict the gender of northern European and African parliamentarians from online images. The dataset used was created from the web, and then gender and Fitzpatrick scale-based skin tone labels were added. This research concluded that despite the type of tool applied, prediction of the gender of females and subjects with dark skin were less accurate compared to males and people with light skin. As a result it was most challenging to predict the gender of black women.  
When using mugshots [18], Caucasians and African American had lower rates of false negative than Asians and American Indians. With respect to age and gender, male and older subjects had lower false negative rates. However, researchers assume those differences are not caused by age or race. In case of comparing high-quality to low-qualityimages of the subjects, false negative rates in general were high, and bias is more difficult to measure with low-quality images. Females were noticed to have higher false negative rates. Finally, by comparing bad quality images, it was found that Africans and Caribbean especially older people had higher false negative rates.