Face Mask Detection using Transfer Learning Technique - Promboonruang, Suksun; Boonrod, Thummarat; Phonthong, Suttipong; Chotpraderm, Thana (KKU Science Journal, 2023)
The face mask detection using transfer learning technique aims to create model for the face mask detection using transfer learning technique and compare the efficiency the models. The tools used are Python language with Keras and Open CV libraries for image processing. The dataset was downloaded from kaggle website, which contained 3,833 images of 150 x 150 pixels image resolution. The images were divided into 1,915 images of faces masks and 1,918 images of faces without masks. The methodology was divided into 3 steps: 1. Data preparation, 2. model generation, and 3. Efficiency evaluation of the 3 models including VGG16, Inceptionresnetv2, and ResNet152V2, using accuracy and loss assessment. The experimental results show that the ResNet152V2 model had the highest accuracy of 98% and the loss of 0.08%.