Assignment Task: Design of Machine Learning Solution for Biometric Recognition Task
A) 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
B) Identify the cases of correct and incorrect outcomes generated by the technique on the given data set
C) 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 cost, 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 using for this assignment 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.
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