Machine Learning Solution for Biometric Recognition Task

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Reference no: EM133102051 , Length: word count:2000

CIS006-2 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 with the highest recognition accuracy. The facial images are taken from real subjects in slightly different conditions, and so some images can be incorrectly recognised. This makes the ideal 100% accurate recognition difficult or even impossible. Students will design a ML solution providing the minimal recognition error.

Examples
Students who studied this unit have achieved excellent results in Biometric Face Recognition published as follows:
1. journal paper
2. conference paper in Springer proceedings
3. Springer book chapter
4. conference paper
Examples of previous assignment reports will also be discussed. Alternatively students can use other benchmark data available in the Kaggle subject area. For example students could be interested in early detection of bone pathologies in X-ray images, as described in a paper published in Scientific Reports.

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 languages related to ML. Alternatively advanced students can use other programming platforms using programming languages such as Python, MATLAB, or R. Advanced students can also be interested in a high performance ML technique such as Deep Learning, Convolutional Networks, and/or Gradient Boosting, demanded on the market. The Google Colab is a recommended platform, however advanced students can use other Integrated Development Environments eg Spyder.

Project Data and Scripts
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.

Individual Reports
Students will run individual experiments by using the project scripts on a benchmark data set. First students are expected to achieve the unit threshold requirements, and then they could develop work to a higher grade. A template for individual reports can be used. Exclude paste&copy to avoid plagiarism.

What do I need to do to pass? (Threshold Expectations from UIF)

1. Create a Colab project account [applicable for other IDEs]

2. Upload the project data and scripts

3. Run the project scripts to build an ANN on the data

4. Analyse and describe the ANN outcomes

5. Total to pass 42%

How do I produce high quality work that merits a good grade?

6. Identify a set of parameters 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 data set

9. Analyse and compare the results of the experiments

Image Processing, ANN techniques, and use cases developed in Colab Python will be considered during lectures and tutorials

Attachment:- Biometric Recognition Task.rar

Attachment:- Practical sessions.rar

Reference no: EM133102051

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