Interpretable Face Contrast Modeling for Smartphone Portrait Rendering

Key points

• The quality of smartphone portrait photography has become increasingly important to both manufacturers and users.1
• Recent devices use complex computational pipelines to improve facial rendering under varied lighting.1
• To support this analysis, we introduce a realistic mannequin-based dataset designed for systematic facial contrast evaluation, comprising 508 full-resolution captures from 29 smartphones under diverse lighting, with region-specific masks and extensive pairwise perceptual annotations.1
• As a downstream analysis, we show that combining the three component-wise metrics provides a meaningful basis for modeling balanced facial contrast preference in a controlled setting, and we present a preliminary evaluation on a smaller real-portrait dataset indicating that insights derived from the mannequin study can extend to real images.1

Sources (1)

• [1]Interpretable Face Contrast Modeling for Smartphone Portrait Rendering
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 02:50 PM

  To address this, we propose an interpretable framework that decomposes face contrast into three components: overall facial tonal separation, eye-region contrast, and micro-contrast of fine skin details.

  The quality of smartphone portrait photography has become increasingly important to both manufacturers and users.

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