The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on social media. To address this challenge, several adversarial attack methods have been proposed to protect individuals from being identified by unauthorized FR systems with perturbed facial images. However, these approaches suffer from poor visual quality or low attack success rates, which limit their practical utility. Recently, diffusion models have achieved tremendous success in image generation. In this work, we ask: can diffusion models be used to generate adversarial examples against FR systems to improve both visual quality and attack performance? We propose DiffProtect, a novel method leveraging a diffusion autoencoder to generate semantically meaningful perturbations on FR systems. Extensive experiments demonstrate that DiffProtect produces more natural-looking encrypted images than state-of-the-art methods while achieving significantly higher attack success rates, e.g. , 24.5 % and 25.1 % absolute improvements on the CelebA-HQ and FFHQ datasets. We further evaluate the effectiveness of DiffProtect in the real world using a commercial FR API and validate its usefulness in practice through a user study. Our code is available at https://github.com/joellliu/DiffProtect .

Gait recognition is a rapidly emerging and significant area of biometrics, leveraging the unique walking patterns of individuals to perform personal identification and facilitate healthcare monitoring, such as elderly care, fall detection, etc. While existing gait recognition methods perform well in indoor, or short-range environments, their effectiveness diminishes significantly when applied to unconstrained outdoor scenarios. Challenges such as environmental turbulence, occlusion, varying viewing angles contribute to this performance drop. To address these challenges and enhance gait recognition accuracy in real-world settings, while also expanding the functionality of gait features for healthcare applications, we propose a unified multitask framework called UniGait. UniGait is designed to perform a comprehensive range of gait analysis tasks, including gait recognition and estimation of gait-related human attributes. UniGait is built upon a transformer-based architecture, which leverages the power of a cross-attention mechanism to simultaneously process multiple sub-tasks. This multitask learning approach allows the model to extract more robust gait features by jointly learning gait recognition and human attribute estimation, leading to improved overall performance. We report the results of extensive experiments and analysis on large-scale, real-world datasets collected under challenging conditions, including long-range (up to 1000 meters) and high-pitch angles (including UAV-based data). The results demonstrated state-of-the-art performance, highlighting the potential of UniGait for deployment in real-world applications, making it a valuable tool for a range of biometric and healthcare monitoring scenarios.

Gait recognition plays a vital role in biometric applications by analyzing the unique characteristics of an individ-ual's walking pattern. Methods based on 2D representations, such as silhouettes and skeletons, are increasingly being developed to learn the shape features and joint dy-namic movements. Nevertheless, the effectiveness of 2D representation-based methods is impeded by factors such as changes in viewpoint, partial occlusion, and noisy en-vironments. 3D representation-based methods can complement 2D representation-based approaches by providing more precise dynamic body shapes and motion information, along with increased robustness against changes in viewpoint and partial occlusion. However, the complex-ity of acquiring accurate 3D representations and the chal-lenges associated with extracting dynamic topological features from sequences of 3D representations hinder the de-velopment of 3D representations-based methods. In this pa-per, we present VM-Gait, a novel multi-modal gait recognition framework that harnesses the advantages of integrating both 2D and 3D representations. Furthermore, we in-troduce a new 3D representation, Virtual Marker, into gait recognition to efficiently learn topological features from 3D representations, avoiding the computational complexi-ties inherent in directly learning from 3D representations like 3D meshes or 3D point clouds. Extensive experiments demonstrate that the proposed framework effectively learns and fuses discriminative information from different gait modalities, enhancing gait recognition performance.

Gait recognition is one of the mainstream approaches for identifying individuals when face information is not available. Most previous methods achieve good performance on structured indoor walking sequences with silhouettes provided. However, when these methods are applied to unconstrained outdoor sequences, a significant reduction in performance is inevitably observed due to factors such as turbulence, occlusion, view angle, and oversized clothing. To make gait recognition methods stable and effective for real-world settings, we extend gait-only-based approaches by introducing more useful biometric information such as gender, age, height, weight, and body mass index to cooperatively work with the gait recognition module. In this paper, we propose a video-based multitasking network for gait recognition and human attribute prediction at ranges of up to 1000 meters and high-pitch angles to mutually improve the robustness and accuracy of each task. Through a series of experiments on OU-MVLP and BRIAR datasets, we show that our multitasking network outperforms previous methods and provides more useful biometric information for human identification tasks.

Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS). 3D Deep Convolutional Neural Network (DCNN) is a viable solution for this task, but is memory intensive. Small isotropic patches are cropped from the original and large CT volume to mitigate this issue in practice, but it may cause discontinuities between the adjacent patches and severe class-imbalances within individual sub-volumes. This paper presents a new 3D DCNN framework, namely Z-Net, to tackle the discontinuity and class-imbalance issue by preserving a full field-of-view of the objects in the XY planes using anisotropic spatial separable convolutions. The proposed Z-Net can be seamlessly integrated into existing 3D DCNNs with isotropic convolutions such as 3D U-Net and V-Net, with improved volume segmentation Intersection over Union (IoU) - up to 12.6%. Detailed validation of Z-Net is provided for CT aortic, liver and lung segmentation, demonstrating the effectiveness and practical value of Z-Net for intra-operative 3D navigation in robot-assisted MIS.