# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 4 Evaluation Methodology
### Second Stage--Face Classification  
To determine whether face-masks are worn correctly or not, all faces detected in the first stage of our pipeline need to be classified into one of two classes: (i) compliant, and (ii) non-compliant, as illustrated in the third image of Figure 4. For the first class we consider faces, where the mask is placed properly and covers the nose, mouth and chin. For the second class, we consider faces that do not have masks at all or have incorrectly placed masks. Under this setup, we crop the facial regions from the input images, rescale them to a standard size (defined by the given model) and then subject them to a recognition procedure. Details on the considered recognition models are given next. **Classification models. ** For the second stage, we consider several recent CNN models that were shown to ensure competitive recognition performance in different problem domains [44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. Specifically, we use the following models in the experimental evaluation:  
* AlexNet [54]--AlexNet is one the first large-scale convolutional neural networks that showed the potential of the deep-learning models ImageNet. The model consists of five conv layers of decreasing filter size followed by three fully connected layers upon which a learning objective can be defined [55]. One of the main characteristics of the model is the very rapid downsampling of the intermediate representations through strided convolutions and max-pooling layers. * VGG-19 [56]--The VGG network is a 19-layer CNN model that uses a series of convolutional layers with small filters sizes \(3\times 3\), to facilitate training. As with AlexNet, the last two layers of VGG-19 are again fully connected and used to define the learning objective for the model. * ResNet [57]--The model is built around the residual CNN architecture and uses skip connections to allow for training of very deep CNN models. We use three ResNet variants with 18 (ResNet-18), 34 (ResNet-34), 50 (ResNet-50) and 101 (ResNet-101) layers. The skip connections allow for efficient training by ensuring appropriate gradient flow. * SqueezeNet [58]--SqueezeNet is a convolutional neural network that employs design strategies to reduce the number of parameters, notably with the use of fire modules that \"squeeze\" parameters using \(1\times 1\) convolutions. In general, SqueezeNet is a special variant of ResNet, optimized for efficiency (in space and time). We used two version of the model for the experiments, the initial SqueezeNet v1.0 from [58], but also SqueezeNet v1.1 ([[https://github.com/forresti/SqueezeNet/tree/master/SqueezeNet_v1.1](https://github.com/forresti/SqueezeNet/tree/master/SqueezeNet_v1.1)]([https://github.com/forresti/SqueezeNet/tree/master/SqueezeNet_v1.1](https://github.com/forresti/SqueezeNet/tree/master/SqueezeNet_v1.1)), accessed on 25 February 2021), which is an improved version that has 2.4\(\times\) less computations and slightly fewer parameters than SqueezeNet v1.0. * DenseNet [59]--DenseNet is a powerful recent CNN model that reuses features from preceding layers in all subsequent layers within a DenseNet block. This architecture makes it possible to learn highly descriptive features that are very different from competing models. Several dense blocks are then concatenated in a ResNet manner to allow for gradient flow and efficient training. * GoogLeNet [60]--GoogLeNet is another type of CNN model based on the Inception architecture. It uses so-called Inception modules, which allow the network to analyze images at different scales within each block. GoogLeNet stacks these modules in a feed forward architecture, with occasional max-pooling layers in between to reduce data dimensionality along the model. * MobileNet [61]--MobileNet is a convolutional neural network architecture optimized for mobile devices. It is based on an inverted residual structure where the residual connections are between bottleneck layers.