A Concise Evaluation of Masked Face Detection Utilizing State-of-the-Art Deep Learning Techniques

Recent years have witnessed remarkable progress in the field of face identification, primarily due to innovative advancements in model construction and the availability of diverse, large-scale datasets. Nonetheless, challenges persist within the detection system, including issues related to variations in stance, scale, lighting conditions, noise, and facial expressions, among others. Among these considerations, occlusion, though inherently complex, has received comparatively less attention than other concerns. This research centers on summarizing the strategies employed by individuals to tackle the challenge of occlusion in the present day. To evaluate the extent to which the aforementioned advancements alleviate the impact of occlusion in facial photographs, two datasets containing masked faces are employed to assess various face identification approaches.