Image Classification using Deep Learning

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CSY3025Artificial Intelligence Techniques

Assignment 1 – Image Classification using Deep Learning

Sentiment analysis through the detection of facial expressions is a useful tool to help understanding consumer mood, emotion, or the degree of concentration in real-time. For instance, car manufactures can use this tool to detect whether a driver is stressed, angry or tired. Teachers can use emotion recognition as a feedback of student engagement. Future computer games can also be designed to adapt game content based on player emotions in order to improve the gaming experience.

Your task is to design and implement an image classifier that can recognise emotions (categories of facial expressions) based on close-up facial images captured from cameras. As a minimum requirement (to support a passing grade), the classifier should have the capability of handling at least 5 (five) different emotions with a reasonable performance. You must also critically analyse any bias in your dataset and model design. To achieve a higher grade, you are expected to design and implement additional features. Examples of additional features are (but not limited to): additional emotion categories, inclusion of different ethnicities, exceptional performance through “in-the-wild” testing, implementations of user interfaces / user applications, data visualisation, comparative analysis of alternative solutions, etc.

To complete the task, you must study the problem space, establish image dataset, design and train a deep learning classifier, and use appropriate methods to evaluate the performance of your classifier.

The deliverables of the assignments include:

1. A working deep learning model implemented using Keras/TensorFlow and Python. You must submit a Python notebook file (.ipynb) with all outputs saved.

2. A report that includes but not limited to the following sections:

a. Cover page

i. A link to your dataset (e.g., github)

b. Introduction

c. Problem analysis and background research

d. Building deep learning network (explain how you make choices at each following step)

i. Dataset

ii. Network (structure, loss function, optimiser, etc.)

iii. Training and evaluation

iv. Testing

e. Summary of additional features (if applies)

f. Discussions and conclusions

i. Lessons learned

ii. Limitations

g. References

3. A 5-minute demo video about your design/implementation

There will be three submission points:

1. Report

2. Source code (Python notebook and application if applicable)

3. Demo video

The marking will be based on:

1. 20% Problem analysis and background research (demonstrate your general knowledge of deep learning and the application scenario)

2. 30% Design (Architectural design of your application, construction of dataset, test plans, etc. including additional features such as extra categories, exceptional model performance, user interface, etc.)

3. 40% Implementation (completion of the model, reasonable performance, and additional features)

4. 10% Report quality (quality of technical writing)

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