Due to privacy and cost issues, the lack of large-scale labeled datasets limits the advancement of person re-identification. Existing methods use generative adversarial networks or game engine rendering for data augmentation to improve re-identification performance. However, these approaches struggle to maintain realistic images. This paper introduces a novel approach called Identity Diffuser, which uses diffusion models to generate synthetic data for the same identity with different poses. Our proposed framework incorporates identity-specific embeddings and target poses into the diffusion process, enabling the generation of realistic and diverse images that consistently preserve identity features. Guided by pretrained re-identification net and target pose heatmap, the framework learns transformation trajectories through forward and backward denoising steps in the diffusion models. This approach effectively maintains key pedestrian attributes across various poses. Experimental results on the Market1501 and DukeMTMC datasets demonstrate a notable improvement in performance, with a 1.73%/0.80% mAp increase in Market1501/DukeMTMC datasets compared with current state-of-the-art method. When less real data is included, the increment can be 5.1%/1.5%, separately.

Human identification at a distance (HID) faces challenges due to the difficulty of acquiring traditional biometric modalities like face and fingerprints. Gait recognition offers a viable solution since it can be captured at a distance. To promote progress in gait recognition and provide a fair evaluation platform, the International Competition on Human Identification at a Distance (HID) has been organized annually since 2020. Since 2023, the competition has adopted the challenging SUSTech-Competition dataset, which includes significant variations in clothing, carried objects, and view angles. No training data is provided, requiring participants to train their models using external datasets. Each year, the competition applies a different random seed to generate distinct evaluation splits, reducing the risk of overfitting and ensuring fair evaluation of cross-domain generalization. Although the previous two competitions (HID 2023 and HID 2024) already utilized this dataset, HID 2025 aimed explicitly to explore whether algorithmic improvements could surpass the accuracy limits observed previously. Despite these heightened challenges, participants again demonstrated significant advancements, with the highest accuracy reaching 94.2%, setting a new benchmark for this dataset. We also analyze key technical trends and outline potential directions for future research on gait recognition.

Biometric recognition systems, known for their convenience, are widely adopted across various fields. However, their security faces risks depending on the authentication algorithm and deployment environment. Current risk assessment methods faces significant challenges in incorporating the crucial factor of attacker’s motivation, leading to incomplete evaluations. This paper presents a novel human-centered risk evaluation framework using conjoint analysis to quantify the impact of risk factors, such as surveillance cameras, on attacker’s motivation. Our framework calculates risk values incorporating the False Acceptance Rate (FAR) and attack probability, allowing comprehensive comparisons across use cases. A survey of 600 Japanese participants demonstrates our method’s effectiveness, showing how security measures influence attacker’s motivation. This approach helps decision-makers customize biometric systems to enhance security while maintaining usability.

Clothing-change person re-identification (CC-ReID) aims to match persons even if they change clothes. Thus, extracting clothing-independent features is the key challenge in CC-ReID. Recently, many researches have primarily focused on auxiliary information to realize discriminative feature learning including soft-biometrics features, such as body shape and gaits, and additional clothes labels. However, this method does not fully capture personal information hidden in RGB images. Owing to pretrained vision-language models like CLIP, textual information can describe a person in such a way that it includes all details to capturing personal characteristics, e.g., gender, age, hairstyle, glass, and clothes. Considering that certain personal textual information remains unchanged in CC-ReID, we propose a novel CC-ReID method called Multi-Masked Prompt Learning(MMPL), which takes full advantage of the ability of CLIP to extract clothing-independent features for CC-ReID. MMPL can extract clothing-independent textual and visual features via Prompt Mask and Visual Mask, respectively. This approach enables to discard clothing information from the trained model. The proposed MMPL outperforms other state-of-the-art approaches, improving the rank-1 accuracy to 1.5% and 1.0% on PRCC and LTCC datasets, respectively.

Person re-identification (ReID), aiming to identify individuals from camera views, often faces challenges such as occlusion and appearance variations by cloth changing. Moreover, due to the long-distance capturing and varying positions of the pedestrians, human face is not always visible thus it is usually neglected in ReID. This paper proposes a novel approach to enhance ReID performance by integrating face and body into a multi-modality ReID framework, particularly improving the behavior in scenarios with occlusion and clothes-changing. Leveraging the visual-linguistic capabilities of the CLIP model, our framework comprises two CLIP-like structures: one dedicated to extracting body appearance features and the other one focused on face features. Furthermore, a feature adapter method is proposed to address the issue of invisible face. Experiments show that state-of-the-art (SOTA) performance is achieved on six popular benchmarks datasets, including Market1501, and LTCC, confirming the superiority of the proposed method. Additionally, we have proposed a multimodality ReID dataset to further verify and analyze the effectiveness of the proposed multi-modality ReID framework.

Marginal softmax loss function has been widely used for face recognition, where a universal angular margin is added between weight prototypes. However, this method neglects the differences between classes and samples. On class-level, the imbalanced real world training dataset requires different margin for the head and tail classes to equally squeeze each class's feature space. On the sample-level, it's also necessary to assign larger importance for the hard samples during training. In this paper, we address these two issues by combining two strategies: (1) explicitly assign the adaptive margin according to the image quantity so that the margin is enlarged for the tail classes; (2) semantically identify the ‘hard positive, samples and misclassified samples [1] to attach adaptive weights to increase the training emphasis on these samples. Extensive experiments on LFW/CFP/AGEDB and IJB-B/IJB-C show our method's effectiveness.

For the sake of better accuracy, the face recognition model is becoming larger and larger, which makes them difficult to be deployed on embedded systems. This work proposes an effective model compression method using knowledge distillation, where a fast student model is trained under the guidance of a complex teacher model. Firstly, different loss combinations and network architectures are analyzed through comprehensive experiments to find the most effective approach. To augment the performance, the feature layer is further normalized to make the optimization objective consistent with cosine similarity metric. Moreover, a teacher weighting strategy is proposed to address the issue when teacher provides wrong guidance. Experimental results show that the student model built by our approach can surpass the teacher model while achieving 3× acceleration.