[论文解读] Biometric Quality: Review and Application to Face Recognition with FaceQnet

Javier Hernandez‐Ortega, Javier Galbally+2 more|arXiv (Cornell University)|Jun 5, 2020

Face recognition and analysis参考文献 49被引用 58

一句话总结

本论文回顾生物识别质量概念并引入 FaceQnet——一种基于深度学习的开源工具，用于从面部图像预测人脸识别的准确性，评估了 v0 与改进的 v1 版本，与最先进指标进行比较。

ABSTRACT

"The output of a computerised system can only be as accurate as the information entered into it." This rather trivial statement is the basis behind one of the driving concepts in biometric recognition: biometric quality. Quality is nowadays widely regarded as the number one factor responsible for the good or bad performance of automated biometric systems. It refers to the ability of a biometric sample to be used for recognition purposes and produce consistent, accurate, and reliable results. Such a subjective term is objectively estimated by the so-called biometric quality metrics. These algorithms play nowadays a pivotal role in the correct functioning of systems, providing feedback to the users and working as invaluable audit tools. In spite of their unanimously accepted relevance, some of the most used and deployed biometric characteristics are lacking behind in the development of these methods. This is the case of face recognition. After a gentle introduction to the general topic of biometric quality and a review of past efforts in face quality metrics, in the present work, we address the need for better face quality metrics by developing FaceQnet. FaceQnet is a novel open-source face quality assessment tool, inspired and powered by deep learning technology, which assigns a scalar quality measure to facial images, as prediction of their recognition accuracy. Two versions of FaceQnet have been thoroughly evaluated both in this work and also independently by NIST, showing the soundness of the approach and its competitiveness with respect to current state-of-the-art metrics. Even though our work is presented here particularly in the framework of face biometrics, the proposed methodology for building a fully automated quality metric can be very useful and easily adapted to other artificial intelligence tasks.

研究动机与目标

• 解释生物识别质量及其对人脸识别的重要性。
• 提出一种基于深度学习的全自动、可扩展的人脸质量度量标准。
• 开发并评估 FaceQnet，使用基于 ICAO 遵从性的地面真实值且无需人工标注。
• 将 FaceQnet 与最先进的人脸质量指标进行比较并提供开源资源。

提出的方法

• 描述将生物识别质量作为识别准确率预测因素的一般框架。
• 通过将深度人脸表征的知识迁移到质量预测器来开发 FaceQnet。
• 使用 ICAO/合规模标签自动创建地面真实值质量分数。
• 将两个 FaceQnet 版本（v0 和 v1）与最先进的指标和 NIST FRVT 独立评估进行对比评估。
• 将 FaceQnet 作为开源项目发布，并提供 LFW 与 VGGFace2 的质量标签。

实验结果

研究问题

• RQ1 如何将生物识别质量量化为人脸识别准确性的预测因素？
• RQ2 基于深度学习的质量度量能否超越现有的手工设计或传统的人脸质量指标？
• RQ3 FaceQnet 的两个版本（v0 和 v1）在与识别性能的一致性方面是否有所改进？
• RQ4 该方法在多个人脸数据库和独立评估中是否具有鲁棒性？
• RQ5 该方法学能否推广到人脸识别以外的其他 AI 任务？

主要发现

• FaceQnet 提供一个从 0 到 1 的数值质量分数，与识别准确性相关。
• FaceQnet v1 在评估中较 v0 有所改进，并在与最先进指标的比较中表现具有竞争力。
• NIST FRVT 的独立评估证实了 FaceQnet 的健全性和竞争力。
• FaceQnet 作为开源发布，便于更广泛的采用和对其他 AI 任务的适应。
• 对 LFW 和 VGGFace2 的质量标签已生成并随 FaceQnet 提供。