论文标题
手机上的面部检测：五个实施和分析
Face Detection on Mobile: Five Implementations and Analysis
论文作者
论文摘要
在许多实际情况下，必须在智能手机或其他高度便携式设备上进行检测。应用程序包括移动面部访问控制系统，驾驶员状态跟踪，情绪识别等。移动设备的处理能力有限，即使在脸部检测应用程序运行的情况下也应具有悠久的电池寿命。因此，在算法质量和复杂性之间达到正确的平衡至关重要。在这项工作中，我们将5种算法适应移动设备。这些算法基于手工制作或基于神经网络的功能，包括：Viola-Jones（Haar Cascade），LBP，Hog，MTCNN，Blazeface。我们分析具有不同输入图像分辨率的不同设备上这些算法的推理时间。我们提供指导，哪些算法最适合移动面部访问控制系统和潜在的其他移动应用程序。有趣的是，我们注意到，级联算法在没有面孔的场景上的性能更快，而在空场景中，Blazeface的表现较慢。利用这种行为在实践中可能很有用。
In many practical cases face detection on smartphones or other highly portable devices is a necessity. Applications include mobile face access control systems, driver status tracking, emotion recognition, etc. Mobile devices have limited processing power and should have long-enough battery life even with face detection application running. Thus, striking the right balance between algorithm quality and complexity is crucial. In this work we adapt 5 algorithms to mobile. These algorithms are based on handcrafted or neural-network-based features and include: Viola-Jones (Haar cascade), LBP, HOG, MTCNN, BlazeFace. We analyze inference time of these algorithms on different devices with different input image resolutions. We provide guidance, which algorithms are the best fit for mobile face access control systems and potentially other mobile applications. Interestingly, we note that cascaded algorithms perform faster on scenes without faces, while BlazeFace is slower on empty scenes. Exploiting this behavior might be useful in practice.
