# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 1 Introduction  
Facial data-based technologies are key applications of artificial intelligence that recently have become significantly more popular. They include face detection [1, 2, 3], facial recognition, gender prediction, age estimation, and emotion prediction. The most beneficial facial data-based technology is facial recognition. It is used in both private and governmental biometric systems due to its essential role in authentication and identification. Most information systems today including economic, political, educational, nuclear, industrial, and security systems employ the face recognition feature to authenticate their users. Moreover, facial identification is globally used by governments and law enforcement agencies to track down criminals and terrorists. Ordinary people have also been affected by the spread of facial recognition technology; they have such features in their own generic devices. However, in spite of the widespread use of facial recognition systems, they can be targets for attackers. Their critical role as a biometric solution for authentication and identification systems increases their vulnerability to face presentation attacks. An attacker can carry out such attacks to perform dangerous actions such as accessing private information, gaining high clearance permissions, manipulating judgment systems, or accessing unauthorized facilities. As a result building strong presentation attack detection (PAD) systems has become a necessity. Throughout the time, facial recognition and presentation attacks detection (PAD) systems have seen many improvements but their major breakthrough came after applying deep learning networks to their models [1; 2; 4]. Since then, facial recognition and presentation attack detection CNN (convolutional neural network)-based models have achieved a significantly higher degree of accuracy and performance compared to traditional models. Deep learning models such as CNN models are based on both machine learning ML and artificial neural networks ANN. They do not require any manual classification, feature extraction, or extensive preprocessing, and that has led to higher accuracy. Their performance and learning ability exceed human performance and increase as the size of data increases. Despite the revolutionary advances that have been achieved in facial detection and recognition through employing deep learning techniques [5; 6], the creditability of these systems is still widely debated by the public and the media. It is claimed that facial technology is biased and demonstrates different results for different demographic groups. Those claims have noticeably risen since the experiments which were conducted on Amazon's facial technology (Rekognition software) resulted in misidentifying over thirty-five American and British parliament members [3]. Consequently, bias in facial recognition became an area of interest for researchers. Researchers such as Singh et al. studied the reliability of current face recognition models in [7]. They demonstrated how face recognition technology is in danger of presentation attacks and demographic bias. Most of the findings indicated biased behavior and showed unfairness in the performance of facial data-based systems toward people from different demographic populations. However, there have not been similar efforts in studying bias in the area of presentation attack detection (PAD) systems. One research was conducted to discover whether iris-based presentation attack detection models are biased or not. Its results showed unfairness in detecting attacks for subjects from different genders [8]. The revealed bias in facial recognition systems and iris-based PAD systems led us to infer the existence of similar demographics unfairness in face-based PAD systems. Therefore, our goal is to investigate bias in hardware independent face PAD systems using CNN models and then exploring the mitigation of potential bias using the debiasing variational autoencoder algorithm proposed by Amini et al., 2019 [9].