深度学习与大数据技术的相遇, 促使人脸识别技术在精度上已经达到很高水平, 然而在实际应用场景中, 尤其在复杂背景、移动中以及自然状态下的人脸识别, 还没有达到令人满意的效果. 针对人脸识别在考勤应用中的问题进行算法设计与改进, 提出递归最小窗口算法, 对M:N多人脸识别场景下人脸跟踪算法进行优化设计, 通过多角度采样提高识别精度和识别鲁棒性, 并在人脸考勤系统中进行应用实现与验证, 取得多人同步3 s内完成考勤的成绩, 在用户体验上获得了较明显的提升.
The encounter between deep learning and big data technology has prompted face recognition technology to achieve a high level of accuracy. However, in actual application scenarios, especially in complex background, moving and natural face recognition, it has not yet achieved people’s satisfactory. Aiming at the problem of face recognition in attendance system, we propose using recursive minimum window algorithm to optimize the design of the face tracking algorithm in the M:N multi-face recognition scenario, and using multi angle sampling to improve the recognition accuracy and robustness. The proposed method is implemented and verified in the face attendance system. Achievement of multi-person synchronization within 3 seconds is achieved, and the user experience has been significantly improved.