Authors - Patil Pratima, Deshpande Deepa Abstract - This paper presents a real-time thermal face detection and recogni-tion system based on an enhanced Multi-task Cascaded Convolutional Neural Network (MTCNN) framework. Unlike visible-light methods, thermal imaging introduces domain-specific challenges such as low spatial resolution, high noise, and intensity variance due to temperature fluctuations. To address these, we propose a dedicated preprocessing pipeline including normalization, contrast enhancement, and channel replication to adapt single-channel thermal images for CNN-based processing. The modified MTCNN is fine-tuned on thermal datasets to accurately detect facial regions and landmarks. Aligned faces are then processed through a thermal-optimized feature embedding network trained with tri-plet loss to produce identity-preserving descriptors. Recognition is performed us-ing a lightweight classifier over the feature space. The system is optimized for real-time performance using GPU acceleration and quantized inference. Experi-mental results on publicly available thermal face datasets demonstrate the effec-tiveness of our approach in terms of detection accuracy, recognition rate, and processing speed, making it suitable for surveillance and biometric applications under low-light or no-light conditions.
