Face detection plays an important role in many artificial intelligence applications, such as identity recognition, facial expression recognition, and gender/age recognition. Recently, the development of deep learning techniques has greatly improved face detection’s performance. However, it is still ineffective and time-consuming to manually design hyperparameters of face detectors for different deployment environments with diverse distributions. Besides, due to the limited computation capability, many previous networks are hard to meet the latency requirements in deployment environments, and the improved resolution of current cameras further increases the computation burden. Motivated by the above problems, we propose a searching framework aiming to automatically search a real-time face detector architecture with a fixed complexity constraint, to adapt a specific deployment environment. We model the whole searching space into two parts, including the hyperparameters of the network and the detector. Instead of only searching the network structure, the proposed method considers the whole model’s hyperparameters space which contains the preprocessing and postprocessing parameters. The evolutionary algorithm is employed to find the optimal solution, and new evolutionary operations are proposed to explore architecture space. During the whole searching procedure, we guarantee the computation cost is under the restrictions. The advantages of the proposed framework are that it considers a hard computation cost constraint and the preprocessing and postprocessing hyperparameters, leading to a fully automatic design style and global optimization. Finally, we evaluate the proposed model on the most popular Widerface and FDDB datasets. The proposed detector significantly surpasses the existing lightweight face detectors in the comprehensive performances, and the average latency is twice as shorter as the best competitor.

人脸检测在许多人工智能应用中发挥着重要作用，例如身份识别、面部表情识别和性别/年龄识别。最近，深度学习技术的发展极大地提高了人脸检测的性能。然而，为具有不同分布的不同部署环境手动设计人脸检测器的超参数仍然是低效且耗时的。此外，由于计算能力有限，许多以前的网络难以满足部署环境中的延迟要求，而当前相机分辨率的提高进一步增加了计算负担。受上述问题的启发，我们提出了一种搜索框架，旨在自动搜索具有固定复杂度约束的实时人脸检测器架构，适应特定的部署环境。我们将整个搜索空间建模为两部分，包括网络的超参数和检测器。所提出的方法不是只搜索网络结构，而是考虑整个模型的超参数空间，其中包含预处理和后处理参数。采用进化算法寻找最优解，并提出新的进化操作来探索架构空间。在整个搜索过程中，我们保证计算成本在限制范围内。所提出框架的优点是它考虑了硬计算成本约束以及预处理和后处理超参数，从而实现了全自动设计风格和全局优化。最后，我们在最流行的 Widerface 和 FDDB 数据集上评估提出的模型。所提出的检测器在综合性能上明显优于现有的轻量级人脸检测器，平均延迟比最佳竞争对手短一倍。