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International Journal of Next-Generation Engineering and Technology

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The Convergence of Spatiotemporal Deep Learning and Trustworthy Biometrics: A Comprehensive Review of Human Activity Recognition, Ethical Governance, And Security Paradigms

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Dr. Simona Kript 1 ,
4 Department of Computer Science and Engineering, University of Melbourne, Australia

Abstract

The rapid evolution of Artificial Intelligence (AI) has catalyzed a paradigm shift in how human behaviors are monitored, analyzed, and secured. This research article provides an extensive investigation into the intersection of spatiotemporal dynamics in human activity recognition (HAR) and the multi-faceted landscape of AI-driven biometrics. By synthesizing a decade of advancements in video action recognition-ranging from 3D convolutional neural networks (3D CNNs) to hybrid Long Short-Term Memory (LSTM) architectures-this study delineates the technical progression of motion analysis. Parallel to these technical strides, we evaluate the socio-technical dimensions of biometric systems, including facial recognition, behavioral biometrics for financial security, and medical clinical applications. Central to this analysis is the concept of "Trustworthy AI," encompassing explainability, fairness across demographic groups, and adversarial robustness. The article explores the ethical tensions between pervasive surveillance and privacy rights, particularly in public spaces and retail environments. Furthermore, we examine the systemic biases inherent in commercial algorithms regarding race and gender, supported by contemporary empirical data. Finally, this work outlines the future research directions necessary to reconcile high-performance activity recognition with the requirements of an AI Bill of Rights, ensuring that the next generation of biometric passports and behavioral security measures are both technically resilient and socially equitable.

Keywords

Human Activity Recognition, Spatiotemporal Dynamics, Trustworthy AI, Behavioral Biometrics

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