Use of triplet loss for facial restoration in low-resolution images
In recent years, facial recognition (FR) models have become the most widely used biometric tool, achieving impressive results on numerous datasets. However, inherent hardware challenges or shooting distances often result in low-resolution images, which significantly impact the performance of FR models. To address this issue, several solutions have been proposed, including super-resolution (SR) models that generate highly realistic faces. Despite these efforts, significant improvements in FR algorithms have not been achieved. We propose a novel SR model FTLGAN, which focuses on generating high-resolution images that preserve individual identities rather than merely improving image quality, thereby maximizing the performance of FR models. The results are compelling, demonstrating a mean value of d' 21% above the best current state-of-the-art models, specifically having a value of d' = 1.099 and AUC = 0.78 for 14x14 pixels, d' = 2.112 and AUC = 0.92 for 28x28 pixels, and d' = 3.049 and AUC = 0.98 for 56x56 pixels. The contributions of this study are significant in several key areas. Firstly, a notable improvement in facial recognition performance has been achieved in low-resolution images, specifically at resolutions of 14x14, 28x28, and 56x56 pixels. Secondly, the enhancements demonstrated by FTLGAN show a consistent response across all resolutions, delivering outstanding performance uniformly, unlike other comparative models. Thirdly, an innovative approach has been implemented using triplet loss logic, enabling the training of the super-resolution model solely with real images, contrasting with current models, and expanding potential real-world applications. Lastly, this study introduces a novel model that specifically addresses the challenge of improving classification performance in facial recognition systems by integrating facial recognition quality as a loss during model training.
Code (0)
등록된 구현이 없습니다.
Tasks
Super-ResolutionTripletMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Blind Face Restoration via Integrating Face Shape and Generative Priors
Blind face restoration, which aims to reconstruct high-quality images from low-quality inputs, can benefit many applications. Although existing generative-based methods achieve significant progress in producing high-…
3D ReconstructionBlind Face RestorationDecoderJoint Face Completion and Super-resolution using Multi-scale Feature Relation Learning
Previous research on face restoration often focused on repairing a specific type of low-quality facial images such as low-resolution (LR) or occluded facial images. However, in the real world, both the above-mentioned fo…
Facial InpaintingGenerative Adversarial NetworkRelationSuper-ResolutionHeavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component Guided Adversarial Learning
With the recent increase in intelligent CCTVs for visual surveillance, a new image degradation that integrates resolution conversion and synthetic rain models is required. For example, in heavy rain, face images captured…
Face ParsingImage ReconstructionImage RestorationImage-to-Image Translation+1AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior
Blind face restoration (BFR) is a fundamental and challenging problem in computer vision. To faithfully restore high-quality (HQ) photos from poor-quality ones, recent research endeavors predominantly rely on facial imag…
8kBlind Face RestorationUnique Faces Recognition in Videos
This paper tackles face recognition in videos employing metric learning methods and similarity ranking models. The paper compares the use of the Siamese network with contrastive loss and Triplet Network with triplet loss…
Face RecognitionMetric LearningTriplet