Popular Models

For face mask recognition, several models can be employed depending on the trade-off between speed and accuracy. This section discusses some of the most effective models used in computer vision for this purpose.

• 1. MobileNetV2: MobileNetV2 is a lightweight convolutional neural network (CNN) that excels in mobile and embedded vision applications. Its architecture is designed to maintain accuracy while significantly reducing the computational load, making it ideal for real-time face mask detection. The model employs depthwise separable convolutions, reducing the number of parameters without compromising on performance. Due to its efficiency, MobileNetV2 is a common choice for edge devices and mobile applications where computational resources are limited.


• 2. ResNet (Residual Networks): ResNet is a deeper CNN architecture that solves the vanishing gradient problem through the use of skip connections, which allow the gradient to flow through the network more easily during backpropagation. This enables the network to have many more layers (e.g., 50, 101, or 152) without suffering from degradation. ResNet is particularly powerful in tasks that require high accuracy, such as face detection and mask recognition. However, its complexity makes it more resource-intensive compared to models like MobileNet.


• 3. VGG16: VGG16 is a classic CNN model known for its simplicity and effectiveness in image classification and object detection. The network consists of 16 layers, primarily composed of convolutional layers followed by fully connected layers. While VGG16 is accurate, it is more computationally expensive than models like MobileNetV2 due to its large number of parameters, making it less ideal for real-time applications on low-power devices. Nonetheless, it remains a solid choice when computational resources are not a concern.


• 4. InceptionNet: InceptionNet, also known as GoogleNet, introduces the concept of Inception modules, which allow the model to capture multi-scale features more efficiently. This is achieved by combining convolutional layers with different kernel sizes in the same block. InceptionNet can be useful for face mask recognition as it captures both global and local features effectively, allowing for robust detection under varying conditions such as lighting changes and partial occlusions.

Each of these models can be fine-tuned using transfer learning techniques, where a pre-trained model is adapted to the specific task of mask detection. Transfer learning accelerates training and improves performance, especially when large labeled datasets are not readily available.