= Introduction

This report focuses on a machine learning task for emotion recognition on human face images. Whilst not novel, it is a useful tool in a variety of applications, such as in marketing, human-robot interactions, healthcare, and security @Emotion-recognition-meta-review. 

== Description of tasks

This is a classification task which predicts the emotion of a person from an image of their face, and outputs the emotion(s) associated with it. 

The range of possible emotions is typically decided by a dataset, though we aim to pick a dataset which contains the emotions of: joy, trust, fear, surprise, sadness, disgust, anger, and anticipation. According to Plutchik's wheel of emotions, a widely accepted model in discrete emotion theory, these are the universally recognized basic emotions @Emotion-recognition-meta-review.

#figure(
  image("emotion-wheel.jpg", width: 91%),
  caption: [Plutchik's wheel of emotions, with the base emotions as well as their amplified/attenuated versions. Intensity increases towards the center and vice versa @Emotion-wheel-source.],
) <emotion-wheel>

We do not use the full range of emotions as it is not practical to train a model to recognize all of them. The base emotions are sufficient for most applications.

For the output format, we want a probability distribution over the classes. This has the benefit of giving more information than single class outputs, can be used to calculate a confidence score, is easily transformed into a positive/negative/neutral output and is easily done (softmax layer). With this, we allow for more model flexibility, which is crucial for deployment to various organization and/or be made accessible via open sourcing.



The dataset used for training is custom-made from online datasets, and the process of producing it will be elaborated in section III.

== Ethical impact, Value Sensitive Design, and use case

AI ethics refers to a set of values and principles that guide the responsible use of AI technologies @AI-ethics-definition, whereas AI bias refers to "computer systems that systematically and unfairly discriminate against certain individuals or groups of individuals" @Bias-main-paper. An ethical impact assessment aims to root out any potential biases in the model by considering stakeholders' values/principles, allowing for fair outcomes for all.

To do this, we employ the use of Value Sensitive Design (VSD). Value in VSD refers to value, defined as "what a group of people consider important in life" @VSD-paper, hence VSD is a methodology which considers the values of both direct and indirect stakeholders in the design of technology.

VSD involves three investigation steps.

+ Conceptual: Identifies stakeholders, what values they hold, how they are affected, discuss trade-offs between values.
+ Empirical: Using quantitative/qualitative methods to expand on the concepts found in the previous step.
+ Technical: Analysis of existing technological mechanism on, and proactive design supporting of human values.

A potential use case of this model is for healthcare surveillance system @Emotion-recognition-medical-surveillance. In hospitals, the model could be used to detect signs of depression or anxiety in patients, and alert the clinic to administer medicine. Compared to traditional methods, such as human observation, the model could be less prone to error and be more available to patients. 

In the hospital example, we must make sure the model is only used on images of patients who have given explicit consent, but how do we get the consent of people who are not mentally well enough to make decisions? Issues like this is beyond our scope of responsibility, but it is important to consider nonetheless.

In the following sections (section II and III), we focus solely on the healthcare use case in hospitals.