Abstract
Face detection is a widely studied problem over the past few decades. Recently, significant improvements have been achieved via the deep neural network, however, it is still challenging to directly apply these techniques to mobile devices for its limited computational power and memory. In this work, we present a proposal generation acceleration framework for real-time face detection. More specifically, we adopt a popular cascaded convolutional neural network (CNN) as the basis, then apply our acceleration approach on the basic framework to speed up the model inference time. We are motivated by the observation that the computation bottleneck of this framework arises from the proposal generation stage, where each level of the dense image pyramid has to go through the network. In this work, we reduce the number of image pyramid levels by utilizing both global and local facial characteristics (i.e., global face and facial parts). Experimental results on public benchmarks WIDER-face and FDDB demonstrate the satisfactory performance and faster speed compared to the state-of-the-arts. %the comparable accuracy to state-of-the-arts with faster speed.
Abstract (translated)
人脸检测是近几十年来广泛研究的一个问题。最近,通过深度神经网络已经取得了显著的改进,但是,由于移动设备有限的计算能力和内存,直接将这些技术应用于移动设备仍然是一个挑战。在这项工作中,我们提出了一个建议生成加速框架的实时人脸检测。更具体地说,我们采用了一种流行的级联卷积神经网络(CNN)作为基础,然后在基本框架上应用我们的加速方法来加快模型推理时间。我们观察到,该框架的计算瓶颈来自提案生成阶段,在该阶段,密集图像金字塔的每个层次都必须通过网络。在这项工作中,我们通过利用全局和局部面部特征(即全局面部和面部部分)来减少图像金字塔级别的数量。公共基准面和FDDB的实验结果表明,与现有技术相比,FDDB具有令人满意的性能和更快的速度。%以更快的速度达到与艺术水平相当的精度。
URL
https://arxiv.org/abs/1904.12094