arXiv AI August 12, 2026 By Sebastian Regalado, Varshanth R. Rao, Ruowei Jiang, Parham Aarabi, Igor Gilitschenski

Towards Unified Dynamic Face Landmark Detection

arXiv:2608. 10346v1 Announce Type: cross Abstract: Although advancements in face landmark detection (FLD) methods continue to push performance boundaries, they overlook two major functional limitations: (1) different network parameters need to be trained independently for each ``$N$-point'' benchmark dataset, and (2) a model trained on an ``$N$-point'' dataset reliably outputs only the $N$ landmarks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Aug 11

Compatibility of Face Embeddings Across Deep Neural Networks

arXiv:2604. 07282v2 Announce Type: replace-cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be trained for domain-specific tasks.

By Fizza Rubab, Yiying Tong, Arun Ross

rag multimodal safety

arXiv AI

Jul 7

PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition

arXiv:2607. 03581v1 Announce Type: cross Abstract: The widespread adoption of facial masks, accelerated by COVID-19 and mandated in security-sensitive settings, has exposed limitations of conventional face recognition systems.

By Dana A Abdullah

llms multimodal

arXiv AI

Jul 10

SwinIFS: Landmark Guided Swin Transformer For Identity Preserving Face Super Resolution

arXiv:2601. 01406v2 Announce Type: replace-cross Abstract: Face super-resolution aims to recover high-quality facial images from severely degraded low-resolution inputs, but remains challenging due to the loss of fine structural details and identity-specific features.

By Habiba Kausar, Saeed Anwar, Omar Jamal Hammad, Abdul Bais

llms benchmarks

arXiv AI

Jun 4

CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems

arXiv:2407. 13922v3 Announce Type: replace-cross Abstract: Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential.

By Guruprasad Viswanathan Ramesh, Ashish Hooda, Shimaa Ahmed, Harrison J Rosenberg, Ramya Korlakai Vinayak, Kassem Fawaz

benchmarks

arXiv Machine Learning

Jun 18

VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset

arXiv:2407. 18245v3 Announce Type: replace-cross Abstract: Human head detection, keypoint estimation, and 3D head model fitting are essential tasks with many applications.

By Orest Kupyn, Eugene Khvedchenia, Christian Rupprecht

diffusion safety

Hugging Face Trending Papers

Jun 23

TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration

Face Video Restoration (FVR) aims to recover high-fidelity facial videos from degraded input while preserving identity and semantic consistency across frames. Existing methods often struggle to simultaneously address three key challenges: identity shift, viewpoint-entangled guidance, and perceptual realism.

rag diffusion benchmarks

Related stories

Compatibility of Face Embeddings Across Deep Neural Networks

PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition

SwinIFS: Landmark Guided Swin Transformer For Identity Preserving Face Super Resolution

CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems

VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset

TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration