Overview: Biometric verification systems are essential in daily life but can have flaws, particularly in misclassifying certain users due to hidden biases. This internship focuses on improving biometric evaluation through explainable AI and visualization techniques.
Key Responsibilities:
Explore explainable visualization methods to identify biases in biometric datasets
Adapt Inter ZooPlot (IZP) and Biometric Confusion Matrix (BCM) for large-scale dataset visualization
Conduct a comparative study on the effectiveness and limitations of various biometric visualizations
Develop a web tool to transform score datasets into visual explainable evaluations
Requirements:
Familiarity with face recognition and keystroke dynamics
Knowledge of biometric modalities
Strong analytical skills and attention to detail
Qualifications:
Pursuing a degree in Computer Science, Data Science, or related field
Experience with visualization techniques and explainable AI is a plus
Duration:
This is an internship position with a flexible duration based on mutual agreement.
Location:
The internship can be conducted remotely or on-site, depending on the candidate's location and availability.