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• Limited exploration of their potential to uncover hidden dataset biases
• Scalability when visualizations are applied to large datasets
• Lack of a systematic comparison across different biometric evaluation visualizations

• Explore explainable visualization methods to uncover potential biases in biometric datasets
• Investigate how IZP and BCM can be adapted for large scale dataset visualization
• Conduct a comparative study of the effectiveness and limitations of different biometric visualizations such as ZooGraph, IZP, and BCM
• Develop a web tool that transforms score datasets into visual explainable evaluations

• Explore visual analytics strategies to improve existing static visualizations for large datasets such as zooming, grouping, and sampling
• Develop a web interface that turns score datasets into interactive explainable evaluations

• Comparative analysis of ZooGraph, IZP, and BCM
• Generate verification score datasets from biased and unbiased biometric datasets using state of the art models
• Apply BCM to analyze biased and unbiased datasets, studying how matrix reordering highlights potential biases
• Explore new matrix reordering strategies beyond VAT for bias discovery

• Zhu, B., & Giot, R. (2025). Biometric Confusion Matrix and Inter ZooPlot: Two Novel Visualizations for Biometric Verification Evaluation. IEEE International Joint Conference on Biometrics (IJCB 2025).
• Yager, N., & Dunstone, T. (2008). The biometric menagerie. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(2), 220–230.
• Giot, R., Bourqui, R., & El-Abed, M. (2016). Zoo graph: A new visualization for biometric system evaluation. IEEE International Conference on Information Visualisation (IV), 190–195.
• Dunstone, T., & Yager, N. (Eds.). (2009). Biometric System and Data Analysis: Design, Evaluation, and Data Mining. Boston, MA: Springer US.