Traditional blood group identification methods, such as serological testing or fingerprint based biometric analysis, require physical contact, specialized equipment and laboratory processing. To remove these boundaries, this study proposes a novel, which is a completely contact -free approach to determine A, B and O blood groups using ocular image analysis. Unlike the previous methods that rely on fingerprint or vein pattern, our technique takes advantage of iris color, conjunctival vasculature, limbal ring intensity, and other eye field features to classify blood group types. A custom dataset of 3,000 eye images was collected from diverse demographics under different lighting conditions. The key features were extracted using hyperspectral imaging and deep learning-based segmentation. We introduce a hybrid multi-modal attention network (HMAN), which integrates transformer-based spatial encoding, convolutional feature extraction, and self-attention mechanisms to enhance classification accuracy. The proposed model obtained 97.1%accuracy, improved ResNet-50 (92.3%) and KushalNet-B4 (94.5%). Ablation studies confirmed that multi-modal feature fusion improves discriminatory capacity for blood group-specific patterns.
This work establishes the first AI-operated, non-invasive blood group detection framework with emergency medical, blood donor screening, and potential applications in biometric diagnostics. Future research will focus on real-time deployment, dataset expansion, and multi-modal physiological feature integration to improve robustness. Our findings represent a major advancement in contact-free medical diagnosis, which paves the way for AI enhance hematological classification.

Menstrual discomfort significantly affects women’s productivity and engagement in structured environments such as offices, schools, colleges, and conferences. However, current access control systems fail to address this issue, resulting to inadequate accommodation. To bridge this gap, we propose an AI-driven menstrual detection system that uses closed-circuit (CC) cameras for facial recognition. Leveraging deep learning, our model analyzes facial cues—such as skin texture changes, eye fatigue, and puffiness to detect discomfort non-invasively while preserving privacy. To support this research, we introduce the Menstrual Presence Facial Dataset (MAFD-2024), a curated collection of facial images captured before and during menstruation, annotated for pain indicators. Our hybrid CNN-LSTM model (HCL-MD) enhances detection accuracy by 94.1%, combining spatial (facial features) and temporal (symptom progression) analysis. This system integrates with automated access frameworks, enabling real-time adjustments for affected individuals. Beyond access control, technology can be embedded in telemedicine for remote discomfort assessment or deployed in smart wearables and surveillance systems (e.g., in schools or public transport) to offer timely suggestions. By enabling discreet, real-time support, this AI solution fosters inclusivity and awareness, pioneering the fusion of facial recognition and menstrual health monitoring.