Research on Multi-dimensional Quality Quantification Standards for Portrait Dataset Images Used in Face Recognition Algorithm Training

Abstract

The training effectiveness of face recognition algorithms is related not only to model architecture and loss functions, but also to the image quality, annotation quality, distributional structure, and compliance governance of portrait datasets. To address the current problems of fragmented quality indicators, a high proportion of subjective screening, and the lack of unified standards for handling low-quality samples in portrait data collection and training admission, this paper proposes a multi-dimensional quality quantification standard for portrait images oriented to algorithm training. Quality is divided into five dimensions: basic imaging quality, face usability, annotation consistency, distributional fairness, and compliance/security. A standardized framework for indicator measurement, graded admission, closed-loop handling, and training feedback is proposed, together with implementable scoring, thresholds, and governance procedures. The study argues that portrait data quality standards should not consider only sharpness or resolution; instead, they should build a quality governance system from the perspectives of algorithmic usability, group balance, and responsibility across the full data lifecycle, so that the standards are explainable, auditable, and re-verifiable.