## 7 Conclusions  
In this work, we first presented an up-to-date review on two-class and three-class mask-face detection models separately to extract the common characteristics and main distinction between the two mentioned fields. We discussed the state-of-the-art approaches and found that researchers prefer one-stage object detection techniques in both two-class and three-class fields for the real-time requirement of masked-face detection applications. We also deduced that research works on three-class masked-face detection models provided new insights, mainly in terms of extending the dataset with more training samples of incorrect mask-wearing or improving models, such as introducing the attention mechanism. A survey on mask-face detection hardware systems was then provided. We summarized that recent hardware system research and mostly contributed in the following three aspects: hardware analysis, system packaging, and model compression. Notably, emphasis of research in this part varied, and no study paid attention to multiple aspects. Therefore, a system which both builds a light-weighted and well-performing detection model and designs a well-adapted and user-friendly hardware platform remains to be developed. Finally, we proposed a new framework named Out-of-Distribution Mask (OOD-Mask). The framework aimed to extract the abnormal cases (i.e., incorrect mask-wearing) from wearing mask and without mask. Thus, the proposed framework succeeded in performing the three-class mask-face detection task based on the data and model required in the two-class mask-face detection task. Developing an integrated mask-face detection system that is low cost, has a fast response, and high accuracy requires techniques in various aspects as we discussed. Based on the survey we conducted, we suggest some research directions for future work. In order to ensure that the detection system can identify whether a person is wearing a mask correctly, researchers need to pay more attention to three-class mask-face detection models. Moreover, in order to make these models compatible with the corresponding hardware systems, instead of increasing model complexity for better accuracy, detection performance should be improved on light-weight models. The OOD framework presented in our work is capable of achieving this, but performance needs to be improved in future work. Furthermore, since the difference between wearing a mask correctly and incorrectly is rather subtle and may be formulated as a fuzzy problem, new fuzzy models [111] could be adopted for this problem in the future. On the hardware side, more hardware research and comparisons are required on how to choose a suitable hardware device to deploy a light-weighted deep learning model. In addition, improving compatibility and achieving better hardware acceleration performance are also significant research directions. Overall, we expect a combination of state-of-the-art techniques both in three-class mask-face detection models and hardware design, as well as continuous efforts in each domain. Conceptualization, Y.H., Y.X. and H.Z. ; methodology, Y.H. and Y.X. ; software: Y.H. ; validation: Y.X. ; formal analysis, Y.H. and Y.X. ; investigation, Y.H. and Y.X. ; resources, Z.W., Z.L. and H.Z. ; data curation, Y.H. and H.Z. ; writing--original draft preparation, Y.H., Y.X. and Z.L. ; writing--review and editing, Y.X., Y.H., Z.L., H.Z. and Z.W. ; visualization, Y.X. ; supervision, Z.L., H.Z. ; project administration, H.Z. and Z.W. ; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Science and Engineering Research Council, Agency of Science, Technology and Research, Singapore, through the National Robotics Program under Grant No. 1922500054.