Partial Face Detection for Continuous Authentication
This addresses the challenge of reliable face detection in real-time mobile authentication scenarios, though it appears incremental as it builds on existing face detection methods.
The paper tackles the problem of detecting partially cropped and occluded faces on mobile devices for continuous authentication by proposing a part-based technique that clusters facial segments, achieving better accuracy and processing speed than many state-of-the-art methods as shown in experiments with 50 users.
In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed. This method is specifically designed for detecting partially cropped and occluded faces captured using a smartphone's front-facing camera for continuous authentication. The key idea is to detect facial segments in the frame and cluster the results to obtain the region which is most likely to contain a face. Extensive experimentation on a mobile dataset of 50 users shows that our method performs better than many state-of-the-art face detection methods in terms of accuracy and processing speed.