# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 5 Results and Discussion
### Classification Results  
In the second series of experiments, we investigate the performance of different recognition models for predicting compliant and non-compliant face-mask placements. We implement multiple versions of the models discussed in Section 4.2 with different numbers of layers--as indicated by the suffixes next to the model names below. We train all models on the training part of FMLD, as summarized in Table 3. All considered models expect (3-channel) RGB images of size H\(\times\)W as input, where H and W are expected to be at least 224. The images are loaded in floating point format (in a range of [0, 1]) and normalized in terms of mean and standard deviation. Here, the vector [0.485, 0.456, 0.406] is used as the mean and [0.229, 0.224, 0.225] as the standard deviation. As already emphasized in the previous section, we perform transfer learning to learn the recognition models. Thus, we initialize the models with weights learned in ImageNet and fine-tune all parameters for the new task. The training data is randomly divided in a ratio of 80:20 into a learning and a validation set. We use 40,307 face images (19,682 compliant, 20,625 non-compliant) for training and 10,077 face images (4921 compliant, 5156 non-compliant) for validation and monitoring model convergence. Cross-Entropy is used  
Figure 8: Example face images that were not detected by RetinaFace (type II errors), the best performing face detector from the experimental evaluation. The detector was adversely affected by extreme face occlusion (top row) and profile and poor-quality faces (bottom row). as the learning objective. For the optimization algorithm, we rely on Stochastic Gradient Descent with a learning rate of 0.001, which is reduced by a factor of 0.1 every 7 epochs, and a momentum of 0.9. The training is repeated 10 times with 10 epochs for each model architecture and with a random division into learning and validation sets. Images are shuffled for each epoch. The final results from the different training runs vary by 0.1% on average on the validation data and best overall model on the validation set is selected for the final performance reporting. With this procedure we ensure that all models are optimized for the best possible performance. In a similar manner as with the detection experiments, we use bootstrapping to generate results with confidence scores. Specifically, we sample test sets of 5000 images from the test part of FMLD 100 times and observe the mean recognition accuracy and the corresponding standard deviation. The results of this experiment are summarized in Table 5. For each tested model, the computational complexity and model size is also reported. Here, the computational complexity is measured through the prediction speed for 12,688 face images on the NVIDIA Titan Xp GPU graphics card with mini-batch size of 128. As can be seen, all tested models achieve an average recognition accuracy of over 97%. The difference between the worst performing model, AlexNet, and the best performing model, ResNet-152, is only 1.12% in absolute terms. These results show that all tested recognition models have sufficient capacity to efficiently discriminate between a compliant and a non-compliant placement of face-masks and can do so very accurately. Although the performance difference between the top performer, ResNet-152, and several other tested models is statistically significant, this difference (around 1%) has limited impact in operational settings, where other aspects of the models may be more important. Figure 9, therefore, compares the tested models with respect to multiple criteria, i.e., processing time, recognition accuracy and model size. Here, the processing time is reported relative to the fastest network (AlexNet). In this comparison, an ideal model would be close to the left and to the top and be represented by a circle with a small diameter. We can see that different models offer different trade-offs. ResNet-152, for example, performs best, but is among the slowest in terms of processing speed.