# Examining Gender Bias of Convolutional Neural Networks via Facial Recognition
## 1 Introduction
#### Literature Review  
This experimental study on facial recognition leans heavily on research and information that has come before. There are decades of documents concerning image technology available on various platforms that investigate all different types of algorithms and models for the purpose of facial identification and recognition. With so much information available, we decided to focus our research on Local Binary Patterns (LBP) and other algorithms associated with that method [12; 13; 14]. However, we came to the conclusion that while LBP was indeed impressive, its efficacy paled in comparison to several newer methods of facial recognition available to us in the literature [15].  
Eventually, we moved on to investigating various Convolutional Neural Network (CNN) models. Different CNN architectures have been around for years, but it is only recently that many have seen inroads into the realm of facial identification and recognition, with results that speak for themselves. The amount of CNNs available to us for research was quite overwhelming. The accuracy levels and efficiency of several CNN models are impressive, and have steadily increased in recent years. Indeed, there are a multitude of CNNs that exist in the literature which have seen great success in the realm of facial identification and recognition [16; 17; 18; 19].  
Several other papers helped shape this manuscript. [PERSON]'s work on facial expression recognition [20] was particularly impressive; his work in creating a new deep learning-based approach was extremely useful with respect to our own research into the topic. The specific feature extraction scheme used by [PERSON]'s group will be considered for our future work with deep networks.  
[PERSON]'s team used a novel approach for facial image classification that involved deep features interspersed with convolutional neural networks [21]. While this work was primarily most effective with smaller datasets for training, it could in theory be utilized even with deeper neural networks and larger datasets, such as those that were utilized in our experiments.  
Emotion recognition is an emerging field that leverages human reaction, along with verbal and non-verbal communication [22]. This complex behavior, when studied with other physical features, can lead to higher accuracy, as well as the potential for a marked increase in robustness of the system. These emotion recognition advances could be extremely useful for future work in this field, specifically with datasets that are tuned to make extensive use of the technology.  
As our research progressed, we decided to select a total of 8 different CNN models which are completely foreign to each other. In other words, we were trying to determine which models are best able to handle gender bias. We decided that we needed to choose a variety of CNN models from different families, rather than 8 models that used virtually the same architecture. It was our hope that this variety in our models would help us to create a new hybrid model consisting of the best parts of several CNNs, which would retain the accuracy and performance that CNNs are known for, while simultaneously being more resistant to different genders of tested individuals.