# Examining Gender Bias of Convolutional Neural Networks via Facial Recognition
## 1 Introduction  
For decades, image recognition technology has made leaps and bounds in the ability to identify individuals in increasingly challenging conditions. We have even seen this technology move into the medical realm, where it is being used for the diagnosis of various different symptoms, from gastrointestinal lesions [1], to diabetic retinopathy [2] with very effective success. Facial biometrics show great promise with regard to authentication systems. This is due to the fact that the human face has specific facial landmarks that allow for systems to accurately discern subjects [3; 4].  
While it would be nice to believe that such security methods are unnecessary or overkill, the reality is that while computer authentication methods have become more advanced over the years, the methods with which computers are attacked have also become more sophisticated. In addition, if an intruder is able to obtain an individual's username and password through nefarious means, then they will be able to gain access to that user's information, regardless of the strength of that user's credentials. The reality is that the simple username/password standard is not a reliable level of security to dissuade attacks on computer systems [5; 6]. Moving into the future, we need to be able to establish systems that make use of different and more effective authentication methods which can use biometrics and/or facial recognition techniques [7; 8; 9].  
This brave new world of facial authentication has shown much promise and has garnered more than its fair share of praise in recent years, However, even the most state-of-the-art facial recognition techniques fall short, specifically where darker complexindividuals and female presenting users are concerned [10]. One study in particular has shown that false positive rates for Western and Eastern African individuals were much higher than that for their Eastern European counterparts [11]. No one can deny the reach of facial recognition technology, and its capacity to do good things in the world; however, we need to make certain that we do everything in our power to mitigate any bias in these systems, to make sure that this technology benefits all people fairly.  
The research proposed in this paper will make use of 8 different advanced Convolutional Neural Networks (CNNs), along with 3 different image datasets to measure the accuracy of those CNNs in recognizing faces from differing genders. This paper will also determine which CNN is the most fairly balanced when it comes to any inherent bias. More information on the CNN models and the datasets utilized in this study can be found in Sections 2.1 and 2.2, respectively.  
Additionally, research into gender bias in facial recognition lacks behind where other areas of research are concerned. The issue of gender bias in facial recognition systems is not that well documented or investigated thus far. As such, this research will ideally lead to increased awareness of the issue of gender bias inherent in some facial recognition technology.  
The accuracy level of the CNNs that are involved in these experiments are well known. However, the overarching purpose of this paper is to examine the potential for those CNNs to have inherent biases, and how we can best deal with those. As such, obtaining the highest possible accuracy in each of our tests is of secondary concern; investigating and mitigating any gender bias is our main objective.  
Finally, as a result of this research, we hope to propose a new CNN model, which will combine some of the best performing CNNs tested in this paper. The purpose of this new CNN model will be to successfully mitigate inherent gender bias, while maintaining an impressive level of accuracy and performance.