Tensor-driven face recognition: Integrating super-resolution and multilinear subspace learning for low-resolution images

Abstract

Low-resolution face recognition poses substantial challenges in computer vision and biometrics, especially in real-world applications such as surveillance, where images are often degraded by limited resolution, low lighting, and noise. These constraints impair facial detail and complicate distinguishing features, making it difficult for conventional recognition systems to achieve high accuracy. This paper introduces a novel approach that combines super-resolution techniques with multilinear subspace learning to enhance recognition accuracy in low-resolution conditions. The proposed methodology employs high-order tensors to represent multidimensional face data and leverages Tensor Cross-View Quadratic Discriminant Analysis (TXQDA) as a powerful multilinear subspace learning tool. We evaluated the system’s performance across two distinct scenarios. First, we assessed very low-resolution face images to examine how multilinear subspace learning impacts recognition accuracy, downscaling face images to three spatial scales (16x16, 32x32, and 48x48). In the second scenario, we applied three advanced super-resolution models Real-ESRGAN, SRGAN, and SRResNet exploring the effects of a 4x upscaling factor on recognition accuracy. Our approach was validated using the Labeled Faces in the Wild (LFW), CelebFaces Attributes (CelebA), and QMUL-SurvFace datasets, achieving substantial accuracy improvements and showcasing its potential for biometric security applications. Our method achieved best-in-class verification rates, achieving 99.17% on the LFW dataset, 90.90% on the CelebA dataset, and 76.90% on the QMUL-SurvFace dataset, underscoring its effectiveness in challenging low-resolution conditions.