2025 |
Ivanovska, Marija; Kreft, Jakob; Štruc, Vitomir; Perš, Janez Privacy-by-design AIoT Vision for Intelligent Urban Environments Journal Article In: Journal of Systems Architecture, 2025. Abstract | Links | BibTeX | Tags: ai, AIoT, computer vision, intelligent environment, privacy @article{MarijaJSA2025,The recent advancements in AI (Artificial Intelligence) have been instrumental in fostering the development of AIoT (Artificial Intelligence of Things)-enabled urban environments. Machine vision and image analysis, in particular, have become integral to a wide array of AI applications within the field of urban planning and monitoring. Yet, the rapid adoption of AI algorithms in public areas has significantly heightened privacy concerns. In this paper, we introduce a privacy-by-design approach tailored for intelligent urban systems, presenting a holistic approach to the development and deployment of AI-driven systems for privacy-preserving image acquisition and analysis. Specifically, we design an embedded vision system that acquires privacy-protected data, safeguarding sensitive information against unauthorized access and potential misuse. Furthermore, we propose a strategy for developing AI vision methods using data that has been anonymized, ensuring that privacy is maintained throughout the AI application building process. Through experiments on a real-world AIoT-enabled urban environment use case - traffic flow monitoring at a city intersection - we demonstrate that our approach upholds strong privacy guarantees while maintaining the operational performance of modern AI vision systems. |
2024 |
Babnik, Žiga; Boutros, Fadi; Damer, Naser; Peer, Peter; Štruc, Vitomir AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation Proceedings Article In: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), pp. 1-6, 2024. Abstract | Links | BibTeX | Tags: ai, CNN, deep learning, face, face image quality assessment, face image quality estimation, face images, face recognition, face verification @inproceedings{Babnik_IWBF2024,Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQA techniques are trained or designed using a specific face alignment procedure. If the alignment technique changes, the performance of most existing FIQA techniques quickly becomes suboptimal. To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures. To validate the proposed distillation approach, we conduct comprehensive experiments on 6 face datasets with 4 recent face recognition models and in comparison to 7 state-of-the-art FIQA techniques. Our results show that AI-KD consistently improves performance of the initial FIQA techniques not only with misaligned samples, but also with properly aligned facial images. Furthermore, it leads to a new state-of-the-art, when used with a competitive initial FIQA approach. The code for AI-KD is made publicly available from: https://github.com/LSIbabnikz/AI-KD. |