PortrAid: An AI-Driven Portrait Assistant for Professional-Quality Image Composition

In the contemporary landscape of ubiquitous portrait photography, achieving professional-quality results remains a significant challenge for many users. This paper introduces PortrAid (portrait aid), an AI-driven portrait assistant system designed to empower users in creating high-quality portraits. Our system employs a modified ResNet50 architecture to analyze portrait shots, adhering to established compositional principles. The proposed method was trained and evaluated on a substantial dataset comprising over 60, 000 portraits, demonstrating precision of 0.9663 in identifying suboptimal portrait captures. Notably, the system exhibits high computational efficiency, processing images within 0.15 seconds on standard hardware. This research not only contributes to advancements in automated image analysis but also democratizes access to professional portrait techniques for users of all skill levels, with practical applications across both amateur and professional photography workflows.

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Published
2025-04-24
DOI
https://doi.org/10.1145/3696673.3723059
Primary Topic
AI in cancer detection
Type
article
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article

PortrAid: An AI-Driven Portrait Assistant for Professional-Quality Image Composition

Gongbo Liang, Jaspal Singh Kahlon
AI in cancer detection
article

PortrAid: An AI-Driven Portrait Assistant for Professional-Quality Image Composition

Gongbo Liang, Jaspal Singh Kahlon
article en

Abstract

In the contemporary landscape of ubiquitous portrait photography, achieving professional-quality results remains a significant challenge for many users. This paper introduces PortrAid (portrait aid), an AI-driven portrait assistant system designed to empower users in creating high-quality portraits. Our system employs a modified ResNet50 architecture to analyze portrait shots, adhering to established compositional principles. The proposed method was trained and evaluated on a substantial dataset comprising over 60, 000 portraits, demonstrating precision of 0.9663 in identifying suboptimal portrait captures. Notably, the system exhibits high computational efficiency, processing images within 0.15 seconds on standard hardware. This research not only contributes to advancements in automated image analysis but also democratizes access to professional portrait techniques for users of all skill levels, with practical applications across both amateur and professional photography workflows.

Texas A&M University – San Antonio (US)
Openalex Percentile: Top 96%
AI in cancer detection
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