提出量化评估证件照肩部姿态的方法，提升自动化审核精度。
A Quantitative Method for Shoulder Presentation Evaluation in Biometric Identity Documents
- 仅用2个肩部关键点3D坐标，计算肩部偏航与翻滚角度。
- 在121张照片上相关性达0.80，与人工标注高度一致。
- 轻量级算法适合部署于身份信息采集系统中。
国际生物特征证件标准要求严格的姿态规范，包括肩部应呈正对镜头的方形呈现。然而，现有自动化质量评估研究中缺乏对此属性的定量方法。本文提出肩部呈现评估（SPE）算法，仅利用常见姿态估计框架提供的两个肩部关键点的3D坐标，量化肩部偏航角与翻滚角。该方法在包含121张人像的照片数据集上进行评估，SPE评分与人工标注标签间表现出较强的皮尔逊相关性（r ≈ 0.80）。通过改进的误差点-剔除分析法验证了该指标在识别不合规样本方面的有效性。所提算法可作为轻量级工具，用于注册系统中的自动合规性检查。
原文摘要 · Abstract (English)
International standards for biometric identity documents mandate strict compliance with pose requirements, including the square presentation of a subject's shoulders. However, the literature on automated quality assessment offers few quantitative methods for evaluating this specific attribute. This paper proposes a Shoulder Presentation Evaluation (SPE) algorithm to address this gap. The method quantifies shoulder yaw and roll using only the 3D coordinates of two shoulder landmarks provided by common pose estimation frameworks. The algorithm was evaluated on a dataset of 121 portrait images. The resulting SPE scores demonstrated a strong Pearson correlation (r approx. 0.80) with human-assigned labels. An analysis of the metric's filtering performance, using an adapted Error-versus-Discard methodology, confirmed its utility in identifying non-compliant samples. The proposed algorithm is a viable lightweight tool for automated compliance checking in enrolment systems.
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