Multi-Metric Evaluation of Thermal-to-Visual Face Recognition
This work addresses face recognition in heterogeneous conditions, such as low-light or thermal imaging, but is incremental as it applies existing GAN and CNN methods to this domain.
The paper tackled cross-spectral face recognition by using GANs to synthesize visual face images from infrared inputs, achieving improved facial feature extraction for identification and verification tasks, with performance evaluated through acceptance rates across similarity measures.
In this paper, we aim to address the problem of heterogeneous or cross-spectral face recognition using machine learning to synthesize visual spectrum face from infrared images. The synthesis of visual-band face images allows for more optimal extraction of facial features to be used for face identification and/or verification. We explore the ability to use Generative Adversarial Networks (GANs) for face image synthesis, and examine the performance of these images using pre-trained Convolutional Neural Networks (CNNs). The features extracted using CNNs are applied in face identification and verification. We explore the performance in terms of acceptance rate when using various similarity measures for face verification.