Reference no: EM132786878

CIS006 Concepts and Technologies of Artificial Intelligence - University of Bedfordshire

Assignment: Design of Machine Learning Solution for Biometric Recognition Task

Learning outcome 1: Demonstrate results of using an established AI technique which is capable of finding a solution to a given AI problem represented by a data set

Learning outcome 2: Identify the cases of correct and incorrect outcomes generated by the technique on the given data set

Learning outcome 3: Evaluate the accuracy of the technique in terms of rates of correct outcomes

Task

Students will develop a Machine Learning (ML) solution to solve a biometric recognition task, capable of providing the highest recognition accuracy. The facial images are taken from real subjects in slightly different conditions, so that the images can be erroneously identified, that makes the ideal 100% accurate recognition difficult or even impossible.

Students will design a ML solution providing the minimal biometric recognition errors.

Method and Technology

To achieve the minimum error, students will use ML techniques such as Artificial Neural Networks (ANNs) which can be implemented by using a new powerful programming platform Google Colab supporting many languages related to ML. Alternatively advanced students can use another programming platform supporting a ML-related language such as Python, MATLAB, or R.

Advanced students can also be interested in high performance ML techniques such as Deep Learning, Convolutional Networks, and/or Gradient Boosting, demanded on the market.

Project Data and Script

The project biometric data include facial images of 30 persons. Each person is represented by 50 images taken under different conditions. When students use Colab, the data zip file has to be uploaded to your Google drive root. The project scripts process_yale_images and classify_yale have to be uploaded to your Colab project.

Alternatively students can use other biometric data benchmarks which are available in a subject area using ML techniques.

Individual Report

Students can work in groups or individually on the Assignment task. In both cases students will need to run individual experiments using the ANN project scripts on the given Biometric Data to meet the unit threshold requirements. Students can further develop their work to a higher grade. A template for individual reports can be used. A similarity level of submitted reports must be < 20%.

Google Colab is a recommended platform, however advanced students can use other development environments

1. Create a Colab project account (5%)

2. Upload the project data and scripts to the account (5%)

3. Using Colab, run the project script to build an ANN on the data (10%) 4 . Analyse and describe the ANN and script outcomes (22%).

5. Total to pass 42%

6. Identify a set of parameters which are required to be adjusted within an ANN technique in order to optimise the solution in terms of recognition accuracy

7. Explain how the ANN parameters influence the recognition accuracy

8. Run experiments in order to verify the solution on a given image set

9. Analyse and compare the results of the experiments.

10. For A-grades (>70%), students will be asked for a 5-min demonstration of developed artefact

Attachment:- Concepts and Technologies of Artificial Intelligence.rar