# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 4 Evaluation Metrics
\[ERR=\frac{FP+FN}{TP+TN+FP+FN}=1-Accuracy\]0 (11) where \(P(r)\) represents the precision at a given recall threshold r, where r ranges from 0 to 1. 16. **mAP (Mean Average Precision)** enhances the notion of AP by aggregating the average precision values across multiple object classes. It offers a unified metric summarizing the overall performance of the object detection model across diverse object categories. Mathematically, mAP is calculated as the average of AP values for all classes, as illustrated in Equation (12). \[ERR=\frac{FP+FN}{TP+TN+FP+FN}=1-Accuracy\]1 (12)
17. **Dice Score** also known as **Dice Coefficient** is a metric commonly used in image segmentation tasks to assess the similarity between two binary masks or segmentation maps. It quantifies the spatial overlap between the ground truth mask (A) and the predicted mask (B), providing a measure of segmentation accuracy. The Dice Score equation compares the intersection of A and B with their respective areas, as defined in Equation (13). \[ERR=\frac{FP+FN}{TP+TN+FP+FN}=1-Accuracy\]2 (13) where \(|A\cap B|\) represents the number of overlapping pixels between the ground truth and predicted masks, while \(|A|\) and \(|B|\) denote the total number of pixels in each mask, respectively. 18. **PSNR (Peak Signal-to-Noise Ratio)** is widely employed in image inpainting to assess the quality of image generation or reconstruction. It quantifies the level of noise or distortion by comparing the maximum possible pixel value to the mean squared error (MSE) between the original and reconstructed images, as depicted in Equation (13). \[ERR=\frac{FP+FN}{TP+TN+FP+FN}=1-Accuracy\]3 (14)
19. **SSIM (Structural Similarity Index Measure)** The Structural Similarity Index Measure [53] evaluates the similarity between two images by considering their luminance, contrast, and structure. It provides a measure of perceptual similarity, accounting for both global and local image features. SSIM is calculated by comparing the luminance, contrast, and structure similarity indexes, as expressed in Equation (15). \[ERR=\frac{FP+FN}{TP+TN+FP+FN}=1-Accuracy\]4 (16) where \(\mu_{R}\) and \(\mu_{F}\) are the mean feature vectors of the real and generated image sets, \(C_{R}\) and \(C_{F}\) are their covariance matrices, and Tr denotes the trace operator. 21. **NIQE (Naturalness Image Quality Evaluator)**[55] assesses the quality of an image based on natural scene statistics. It evaluates the level of distortions introduced during image acquisition or processing, providing a measure of image fidelity. NIQE computes the deviation of the image from the expected natural scene statistics, with higher scores indicating greater image distortion. 22. **BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator)**[56] is a no-reference image quality assessment metric. It evaluates the perceived quality of an image by analyzing its spatial domain features, such as local sharpness and contrast. BRISQUE computes a quality score based on the statistical properties of these features, with lower scores indicating higher image quality.