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Privacy concerns in the digital era highlight the sensitivity of facial images, revealing personal information beyond the identity. The current face deidentification techniques aiming to solve the issue often suffer from limitations. They either focus on narrow facial areas, compromise the image naturalness, lack flexibility in privacy levels, or offer suboptimal tradeoffs between the identity protection, image quality, and utility preservation. The paper presents a novel controllable face deidentification method that leverages StyleGAN2 and auxiliary classification approaches addressing these shortcomings. Validated across four datasets and compared to seven competitors, the method ensures a significant identity protection, preserves the data utility, maintains the diversity among deidentified faces, and demonstrates a promising performance. [ABSTRACT FROM AUTHOR]

The paper proposes an augmented reality system for visual object verification that helps warehouse workers perform their work. The system sequentially captures images of objects that the warehouse workers encounter during their work and verifies whether the objects are the ones that the workers are supposed to fetch from storage. The system uses Androidpowered smart glasses to capture image data and display results to the user, whereas the computationally-intensive verification task is carried out in the cloud and is implemented using recent deep-learning techniques. By doing so, the system is able to process images in near real-time and achieves a high verification accuracy as shown by the experimental results. [ABSTRACT FROM AUTHOR]