Are you planning to write a module that plots the following based on a predefined data format?
ROC curve
DET curve
CMC curve
FAR vs. FRR curve
Precision-Recall curve
EER point
AUC for ROC curve
Confusion Matrix
One suggestion would be to structure data in a table format (e.g., CSV), with columns representing the following:
-
subject_id: A unique identifier for the individual.
-
modality: The biometric modality (e.g., fingerprint, face, iris, etc.).
-
instance: A unique identifier for the specific biometric instance (e.g., a specific fingerprint image or facial scan). This is useful when multiple instances are collected per subject.
-
score: A similarity or matching score that quantifies the similarity between the biometric template and the input data. Higher scores indicate a better match.
-
genuine: A binary value (1 for genuine and 0 for imposter) indicating whether the match is a genuine match (same subject) or an imposter match (different subjects).
This format should accommodate most biometric modalities, as it captures the core components of biometric performance evaluations. Depending on the specific modality, it may be necessary to include additional columns to capture modality-specific information. For example, fingerprint data may require information about the type of sensor used or the quality of the image, while face recognition data may need to include information about the pose, expression, or lighting conditions.
Are you planning to write a module that plots the following based on a predefined data format?
ROC curve
DET curve
CMC curve
FAR vs. FRR curve
Precision-Recall curve
EER point
AUC for ROC curve
Confusion Matrix
One suggestion would be to structure data in a table format (e.g., CSV), with columns representing the following:
subject_id: A unique identifier for the individual.
modality: The biometric modality (e.g., fingerprint, face, iris, etc.).
instance: A unique identifier for the specific biometric instance (e.g., a specific fingerprint image or facial scan). This is useful when multiple instances are collected per subject.
score: A similarity or matching score that quantifies the similarity between the biometric template and the input data. Higher scores indicate a better match.
genuine: A binary value (1 for genuine and 0 for imposter) indicating whether the match is a genuine match (same subject) or an imposter match (different subjects).
This format should accommodate most biometric modalities, as it captures the core components of biometric performance evaluations. Depending on the specific modality, it may be necessary to include additional columns to capture modality-specific information. For example, fingerprint data may require information about the type of sensor used or the quality of the image, while face recognition data may need to include information about the pose, expression, or lighting conditions.