Impact of Face Alignment on Face Image Quality
Eren Onaran, Erdi Sar{\i}ta\c{s}, Haz{\i}m Kemal Ekenel

TL;DR
This study investigates how face alignment influences face image quality scores, revealing that quality assessment methods are sensitive to alignment, especially under challenging real-world conditions, impacting facial analysis performance.
Contribution
The paper provides a comprehensive analysis of the impact of face alignment on face image quality assessment, highlighting its significance in real-world applications.
Findings
Face quality scores are affected by alignment methods.
Sensitivity to alignment increases in challenging conditions.
Alignment impacts the reliability of face quality metrics.
Abstract
Face alignment is a crucial step in preparing face images for feature extraction in facial analysis tasks. For applications such as face recognition, facial expression recognition, and facial attribute classification, alignment is widely utilized during both training and inference to standardize the positions of key landmarks in the face. It is well known that the application and method of face alignment significantly affect the performance of facial analysis models. However, the impact of alignment on face image quality has not been thoroughly investigated. Current FIQA studies often assume alignment as a prerequisite but do not explicitly evaluate how alignment affects quality metrics, especially with the advent of modern deep learning-based detectors that integrate detection and landmark localization. To address this need, our study examines the impact of face alignment on face image…
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Taxonomy
TopicsFace recognition and analysis
