Department of Otolaryngology-Head and Neck Surgery, University of Kentucky.
Department of Computer Science, University of Kentucky, Lexington, KY.
J Craniofac Surg. 2022;33(8):2443-2446. doi: 10.1097/SCS.0000000000008817. Epub 2022 Aug 15.
Facial recognition software (FRS) is becoming pervasive in society for commercial use, security systems, and entertainment. Alteration of the facial appearance with surgery poses a challenge to these algorithms, but several methods are being studied to overcome this issue. This study systematically reviews methods used in facial recognition of surgically altered faces.
A systematic review was performed by searching PubMed and Institute of Electrical and Electronics Engineers (IEEE) databases to identify studies addressing FRS and surgery. On initial review, 178 manuscripts were identified relating to FRS and surgery and allowed division into multiple subgroups. The decision was made to focus on the recognition of surgically altered faces.
Eligible studies included those reports in English on FRS of surgically altered faces, and 39 papers were included. Surgical procedures range from affecting skin surface, such as skin peeling, to altering facial features, such as rhinoplasty, mentoplasty, malar augmentation, brow lift, facelift, orthognathic surgery, facial reanimation, and facial feminization. Methods were classified into appearance-based, feature-based, and texture-based. Descriptive versus experimental protocols were characterized by different reporting outcomes and controls. Accuracy ranged from 19.1% to 85.35% using various analysis methods.
Knowledge of available limitations and advantages can aid in counseling patients regarding personal technology use, security, and quell fears about surgery to evade authorities. Surgical knowledge can be utilized to improve FRS algorithms for postsurgical recognition.
人脸识别软件（FRS）在商业用途、安全系统和娱乐领域已经变得无处不在。通过手术改变面部外观对这些算法提出了挑战，但目前正在研究几种方法来克服这个问题。本研究系统地回顾了用于识别整容后面部的方法。
通过搜索 PubMed 和 Institute of Electrical and Electronics Engineers（IEEE）数据库，进行了系统评价，以确定涉及 FRS 和手术的研究。在初步审查中，确定了 178 篇与 FRS 和手术相关的手稿，并允许分为多个子组。决定专注于识别整容后面部。
合格的研究包括关于整容后面部 FRS 的英语报告，共纳入 39 篇论文。手术程序范围从影响皮肤表面（如皮肤剥落）到改变面部特征（如隆鼻术、颏成形术、颧骨增高术、眉提升术、面部提升术、正颌外科、面部神经再支配术和面部女性化）。方法分为基于外观、基于特征和基于纹理的。描述性和实验性方案的特点是不同的报告结果和对照。使用各种分析方法，准确性范围从 19.1%到 85.35%。
了解可用的局限性和优势可以帮助患者在个人技术使用、安全和避免因逃避当局而进行手术方面提供咨询。手术知识可用于改进 FRS 算法以进行术后识别。
