Vision-Based AI Solutions for Human Life and Social Science: From Image Processing to Human Behavior Modeling

W. A. Jarvis

doi:10.71222/pgdch948

Authors

W. A. Jarvis Department of Computer Science, Australian National University, Canberra, Australia Author

DOI:

https://doi.org/10.71222/pgdch948

Keywords:

social science, computer vision, pattern recognition, artificial intelligence

Abstract

Artificial Intelligence (AI) has become a transformative force in social science research, enabling the analysis of large-scale, heterogeneous data to uncover latent patterns and predict complex human behaviors. Among AI’s core methodologies, computer vision has evolved far beyond its early role in data acquisition to now encompass sophisticated systems capable of interpreting, analyzing, and synthesizing visual information across a range of socially relevant contexts. By integrating advanced image processing, machine learning, and computer graphics, computer vision empowers interdisciplinary investigations in psychology, sociology, and economics, modeling phenomena such as decision-making, emotional expression, and social interaction at unprecedented scales and resolutions. This paper presents a comprehensive survey of the state of the art in computer vision applications within the social sciences, with particular emphasis on recent breakthroughs in algorithms and enabling technologies that facilitate automated visual understanding. Key topics include object detection, facial recognition, scene understanding, and predictive modeling, which collectively underpin impactful applications in healthcare, autonomous systems, surveillance, and digital media. To structure this rapidly expanding domain, we propose a conceptual framework that organizes the field into four foundational pillars: image processing, object recognition, adaptive machine learning, and computer graphics. Each pillar contributes critical capabilities such as feature extraction, quality enhancement, semantic interpretation, and photorealistic rendering — functions that are increasingly pivotal in addressing contemporary social challenges. By critically evaluating current methodologies, benchmarking performance across domains, and identifying emergent trends, this work not only synthesizes existing knowledge but also outlines promising directions for future research at the intersection of AI and social science. Finally, we highlight how these advancements are reshaping societal norms and enabling AI-driven solutions to pressing global issues, from autonomous navigation to public health and beyond.

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