# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 4 Evaluation Metrics
It serves as a basis for computing various evaluation metrics and identifying areas for improvement. FAR (False Acceptance Rate) serves as a focused gauge of security vulnerabilities, offering precise insights into the system's efficacy in thwarting unauthorized access attempts. This pivotal metric plays a crucial role in evaluating the overall security effectiveness of biometric authentication systems, thereby guiding strategic endeavors aimed at bolstering the system reliability and mitigating security threats. Equation (6) delineates its formula, providing a quantifiable framework for assessing system performance. \[FAR=\frac{FP}{FP+TN}\] (6)
10. **FRR (False Rejection Rate)** is a crucial metric for evaluating system usability, representing the likelihood of the system inaccurately rejecting a legitimate identity match. Its assessment is integral to gauging the user-friendliness of the system, with a high FRR indicating diminished usability due to frequent denial of access to authorized individuals. Conversely, achieving a lower FRR is essential for improving user satisfaction and optimizing access procedures. The calculation of FRR is depicted in Equation (7). \[FRR=\frac{FN}{FN+TP}\] (7)
11. **EER (Equal Error Rate)** denotes the threshold on the ROC curve where the false acceptance rate (FAR) equals the false rejection rate (FRR), signifying the equilibrium point between false acceptance and false rejection rates. A lower EER signifies a superior performance in achieving a balance between these two error rates. 12. **Specificity** also known as the **true negative rate** gauges the system's proficiency in accurately recognizing negative instances. Specifically, it assesses the system's capability to correctly identify individuals who are not the intended subjects. Mathematically, specificity is calculated using Equation (8). This metric offers valuable insights into the system's performance in correctly classifying negatives, contributing to its overall effectiveness and reliability. \[Specificity=\frac{TN}{TN+FP}=1-FAR\] (8)
13. **Rank-N Accuracy** is a widely employed metric in facial recognition tasks, that assesses the system's capability to prioritize the correct match within the top N retrieved results. It quantifies the percentage of queries for which the correct match is positioned within the top-N-ranked candidates. In the Rank-N Identification Rate evaluation, the system's output is considered accurate if the true identity of the input is within the top N identities listed by the system. For instance, in a Rank-1 assessment, the system is deemed correct if the true identity occupies the top spot. Conversely, in a Rank-5 evaluation, the system is considered accurate if the true identity is among the top 5 matches. A higher Rank-N Accuracy signifies superior performance in identifying the correct match among the retrieved candidates, providing valuable insights into the system's efficacy in real-world scenarios. Mathematically, it is represented as depicted in Equation (9). \[Rank-NAccuracy=\frac{\text{\# correct matches within top}-N}{\text{Total number of queries}}\] (9)
14. **Intersection over Union (IoU)** quantifies the extent of spatial overlap between the predicted bounding box (P) and the ground truth bounding box (G). Its mathematical representation is shown in Equation (10). \[IoU=\frac{P\cap G}{P\cup G}\] (10)15. **AP (Average Precision)** serves as a crucial measure in assessing object detection systems. It provides insight into how effectively these systems perform across different confidence thresholds, by evaluating their precision-recall performance. AP computes the average precision across all recall values, indicating the model's ability to accurately detect objects at varying confidence levels. This calculation involves integrating the precision-recall curve, as demonstrated in Equation (11). By considering the precision-recall trade-off comprehensively, AP offers a holistic evaluation of the detection method's effectiveness.