arXiv subjects

Mohammad Mehdi Hosseini

Publications and source records attributed to Mohammad Mehdi Hosseini.

5 recordsLinked to original sources

arXiv2026

Faces of Fairness: Examining Bias in Facial Expression Recognition Datasets and Models

Automated Facial Expression Recognition (FER), involves two critical aspects: data and model design. Both significantly influence bias and fairness in FER tasks. However, issues related to bias and fairness in FER datasets and models remain underexplored. This study investigates bias and fairness in FER datasets and models. The bias of four common in-the-wild FER datasets, including AffectNet, ExpW, Fer2013, and RAF-DB, is studied. Additionally, this research evaluates the bias and fairness of seven deep models, including three generic CNN models: MobileNet, ResNet, XceptionNet, as well as two popular Transformer-based models: ViT and CLIP, plus two FER-specific state-of-the-art models: POSTER and CEPrompt. Unlike prior studies that examine only limited aspects of bias, our work introduces a unified evaluation framework for FER that integrates five existing and two newly proposed dataset metrics with four fairness criteria for model analysis. We further introduce two new metrics, Conditional-Entropy Bias Index and Concentration Index, designed to quantify conditional dependencies and intra-group data imbalance that existing measures fail to capture. Our results show that all four datasets carry significant demographic bias, most notably in race, with AffectNet exhibiting the highest overall bias and Fer2013 the lowest. At the model level, we find that residual-based CNN architectures (ResNet and XceptionNet) exhibit the lowest overall bias, whereas Transformer-based models (ViT and CLIP) exhibit the highest, despite often achieving comparable or superior accuracy. These findings demonstrate that high predictive accuracy does not guarantee fairness, and that dataset-level and model-level bias must be addressed jointly rather than in isolation.

cs.CV↗

arXiv2026

Language-Based Digital Twins for Elderly Cognitive Assistance

Digital twins have emerged as a promising paradigm for personalized healthcare, enabling modeling of individual behavior and health trajectories. In cognitive health, early detection of Mild Cognitive Impairment (MCI) remains challenging, where language and conversational patterns serve as non-invasive biomarkers. In this work, we propose a language-based digital twin framework that leverages large language models (LLMs) to mimic the conversational behavior of elderly individuals by incorporating stylometric cues and contextual metadata. To evaluate fidelity and cognitive consistency, we introduce a multi-head conditional variational autoencoder (cVAE) that jointly measures reconstruction quality and predicts cognitive scores. Experiments on the I-CONECT dataset show that the digital twin preserves identity-specific characteristics and achieves reconstruction and MoCA prediction errors comparable to real data, while outperforming baseline GPT-generated responses. These results highlight the potential of language-based digital twins as a scalable and non-invasive approach for personalized and continuous cognitive health monitoring.

cs.AI↗

arXiv2026

Margin-Consistent Deep Subtyping of Invasive Lung Adenocarcinoma via Perturbation Fidelity in Whole-Slide Image Analysis

Whole-slide image classification for invasive lung adenocarcinoma subtyping remains vulnerable to real-world imaging perturbations that undermine model reliability at the decision boundary. We propose a margin consistency framework evaluated on 203,226 patches from 143 whole-slide images spanning five adenocarcinoma subtypes in the BMIRDS-LUAD dataset. By combining attention-weighted patch aggregation with margin-aware training, our approach achieves robust feature-logit space alignment measured by Kendall correlations of 0.88 during training and 0.64 during validation. Contrastive regularization, while effective at improving class separation, tends to over-cluster features and suppress fine-grained morphological variation; to counteract this, we introduce Perturbation Fidelity (PF) scoring, which imposes structured perturbations through Bayesian-optimized parameters. Vision Transformer-Large achieves 95.20 +/- 4.65% accuracy, representing a 40% error reduction from the 92.00 +/- 5.36% baseline, while ResNet101 with an attention mechanism reaches 95.89 +/- 5.37% from 91.73 +/- 9.23%, a 50% error reduction. All five subtypes exceed an area under the receiver operating characteristic curve (AUC) of 0.99. On the WSSS4LUAD external benchmark, ResNet50 with an attention mechanism attains 80.1% accuracy, demonstrating cross-institutional generalizability despite approximately 15-20% domain-shift-related degradation and identifying opportunities for future adaptation research.

cs.CV↗

arXiv2024

AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels

Automated Facial Expression Recognition (FER) is challenging due to intra-class variations and inter-class similarities. FER can be especially difficult when facial expressions reflect a mixture of various emotions (aka compound expressions). Existing FER datasets, such as AffectNet, provide discrete emotion labels (hard-labels), where a single category of emotion is assigned to an expression. To alleviate inter- and intra-class challenges, as well as provide a better facial expression descriptor, we propose a new approach to create FER datasets through a labeling method in which an image is labeled with more than one emotion (called soft-labels), each with different confidences. Specifically, we introduce the notion of soft-labels for facial expression datasets, a new approach to affective computing for more realistic recognition of facial expressions. To achieve this goal, we propose a novel methodology to accurately calculate soft-labels: a vector representing the extent to which multiple categories of emotion are simultaneously present within a single facial expression. Finding smoother decision boundaries, enabling multi-labeling, and mitigating bias and imbalanced data are some of the advantages of our proposed method. Building upon AffectNet, we introduce AffectNet+, the next-generation facial expression dataset. This dataset contains soft-labels, three categories of data complexity subsets, and additional metadata such as age, gender, ethnicity, head pose, facial landmarks, valence, and arousal. AffectNet+ will be made publicly accessible to researchers.

cs.CV↗

arXiv2023

Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary Learning

In most image retrieval systems, images include various high-level semantics, called tags or annotations. Virtually all the state-of-the-art image annotation methods that handle imbalanced labeling are search-based techniques which are time-consuming. In this paper, a novel coupled dictionary learning approach is proposed to learn a limited number of visual prototypes and their corresponding semantics simultaneously. This approach leads to a real-time image annotation procedure. Another contribution of this paper is that utilizes a marginalized loss function instead of the squared loss function that is inappropriate for image annotation with imbalanced labels. We have employed a marginalized loss function in our method to leverage a simple and effective method of prototype updating. Meanwhile, we have introduced ${\ell}_1$ regularization on semantic prototypes to preserve the sparse and imbalanced nature of labels in learned semantic prototypes. Finally, comprehensive experimental results on various datasets demonstrate the efficiency of the proposed method for image annotation tasks in terms of accuracy and time. The reference implementation is publicly available on https://github.com/hamid-amiri/MCDL-Image-Annotation.

cs.CV↗