A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification

Person Re-identification (ReID) systems identify individuals across images or video frames and play a critical role in various real-world applications. However, many ReID methods are influenced by sensitive attributes such as gender, pose, and body mass index (BMI), which vary in uncontrolled environments, leading to biases and reduced generalization. To address this, we extend the concept of expressivity to the body recognition domain to better understand how ReID models encode these attributes. Expressivity, defined as the mutual information between feature vector representations and specific attributes, is computed using a secondary neural network that takes feature and attribute vectors as inputs. This provides a quantitative framework for analyzing the extent to which sensitive attributes are embedded in the model's representations. We apply expressivity analysis to SemReID, a state-of-the-art self-supervised ReID model, and find that BMI consistently exhibits the highest expressivity scores in the model's final layers, underscoring its dominant role in feature encoding. In the final attention layer of the trained network, the expressivity order for body attributes is BMI > Pitch > Yaw > Gender, highlighting their relative importance in learned representations. Additionally, expressivity values evolve progressively across network layers and training epochs, reflecting a dynamic encoding of attributes during feature extraction. These insights emphasize the influence of body-related attributes on ReID models and provide a systematic methodology for identifying and mitigating attribute-driven biases. By leveraging expressivity analysis, we offer valuable tools to enhance the fairness, robustness, and generalization of ReID systems in diverse real-world settings.

PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks

Psoriasis is a chronic skin condition that requires long-term treatment and monitoring. Although, the Psoriasis Area and Severity Index (PASI) is utilized as a standard measurement to assess psoriasis severity in clinical trials, it has many drawbacks such as (1) patient burden for in-person clinic visits for assessment of psoriasis, (2) time required for investigator scoring and (3) variability of inter- and intra-rater scoring. To address these drawbacks, we propose a novel and interpretable deep learning architecture called PSO-Net, which maps digital images from different anatomical regions to derive attention-based scores. Regional scores are further combined to estimate an absolute PASI score. Moreover, we devise a novel regression activation map for interpretability through ranking attention scores. Using this approach, we achieved inter-class correlation scores of 82.2% [95% CI: 77- 87%] and 87.8% [95% CI: 84-91%] with two different clinician raters, respectively.

Gaussian Harmony: Attaining Fairness in Diffusion-based Face Generation Models

Diffusion models have achieved great progress in face generation. However, these models amplify the bias in the generation process, leading to an imbalance in distribution of sensitive attributes such as age, gender and race. This paper proposes a novel solution to this problem by balancing the facial attributes of the generated images. We mitigate the bias by localizing the means of the facial attributes in the latent space of the diffusion model using Gaussian mixture models (GMM). Our motivation for choosing GMMs over other clustering frameworks comes from the flexible latent structure of diffusion model. Since each sampling step in diffusion models follows a Gaussian distribution, we show that fitting a GMM model helps us to localize the subspace responsible for generating a specific attribute. Furthermore, our method does not require retraining, we instead localize the subspace on-the-fly and mitigate the bias for generating a fair dataset. We evaluate our approach on multiple face attribute datasets to demonstrate the effectiveness of our approach. Our results demonstrate that our approach leads to a more fair data generation in terms of representational fairness while preserving the quality of generated samples.