论文标题
部分可观测时空混沌系统的无模型预测
An Efficient Method for Face Quality Assessment on the Edge
论文作者
论文摘要
实际上，面部识别应用由两个主要步骤组成：面部检测和特征提取。在基于唯一的基于视觉的解决方案中，第一步通过摄入相机流来生成单个身份的多个检测。边缘设备的实用方法应根据其符合认可的身份来优先考虑这些身份的这些检测。从这个角度来看，我们只需将单层附加到面部标志性检测网络，就提出了面部质量得分回归。几乎没有额外的成本，可以通过训练单层以通过增强（例如增强）来回归识别得分来获得面部质量得分。我们通过所有面部检测管道步骤，包括检测，跟踪和对齐方式，在Edge GPU上实施了建议的方法。全面的实验表明，通过与SOTA面部质量回归模型进行比较，在不同的数据集和现实生活中进行了比较，该方法的效率。
Face recognition applications in practice are composed of two main steps: face detection and feature extraction. In a sole vision-based solution, the first step generates multiple detection for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detection of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just appending a single layer to a face landmark detection network. With almost no additional cost, face quality scores are obtained by training this single layer to regress recognition scores with surveillance like augmentations. We implemented the proposed approach on edge GPUs with all face detection pipeline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach's efficiency through comparison with SOTA face quality regression models on different data sets and real-life scenarios.
