Detecting Multi-Pose Masked Face Using Adaptive Boosting and Cascade Classifier

This paper presents the use of the Adaptive Boosting and Cascade Classifier based method to detect someone wearing a mask or not on various facial poses. In general, masks are used to protect the nose and mouth to prevent dirt or bacteria or virus from entering the respiratory tract Simply stated, a person is said to use a mask well if the nose and mouth areas are not visually visible on the face. OpenCV has already provided a model for human face detection for both frontal and profile faces. These models are good for detecting human frontal faces and profile faces, but not to detect people with masks. To deals with multi-pose people's faces using masks, we have to train new nose and mouth-based Adaptive Boosting and Cascade Classifies based model on various face poses independently to achieve our expectation. The Caspeal face database and AISL face database are used to train the Haar-like feature, LBP feature, and HOG feature based Adaptive Boostings and Cascade Classifiers. Based on the results of experiments, Haar-like feature outperforms LBP feature and HOG feature by achieving the best detection accuracy of 86.9%. On the other hand, LBP features outperforms Haar- like feature and HOG feature in computation time by achieving less than 30 msec from image loading until showing the detection result.