The Best 36 Face-related Tools in 2025

A facial emotion recognition model fine-tuned based on Vision Transformer (ViT), supporting 7 expression classifications

A facial emotion recognition model fine-tuned based on Google's ViT-base model, achieving 91% accuracy on the test set.

A high-precision gender classification model based on face images, with an accuracy of approximately 98.7%

ViT-based facial expression recognition model, fine-tuned on FER2013, MMI, and AffectNet datasets, supporting seven emotion classifications

A ViT-based facial expression recognition model fine-tuned on FER2013, MMI, and AffectNet datasets, capable of recognizing seven basic emotions

ResMaskNet is a convolutional neural network that combines residual masking mechanisms with a U-Net architecture for facial emotion recognition.

A Vision Transformer-based facial attractiveness scoring model that returns a score between 0 and 1 for input facial images, with an accuracy of approximately 83%.

This model was trained by Rudolf Enyimba for detecting deepfake images, achieving a test accuracy of 98.70%.

A facial expression recognition model fine-tuned based on a pre-trained model, which can effectively recognize eight different facial expressions.

MiewID-msv3 is a contrastive learning-trained feature extractor for individual re-identification across 64 terrestrial and aquatic wildlife species on a large-scale high-quality dataset.

A model trained using Vision Transformer (ViT) architecture to predict age ranges from facial images

A PyTorch-based facial emotion recognition model trained on the FER2013 dataset with an accuracy of 91.9%.

A facial expression recognition model based on ViT architecture, fine-tuned on the imagefolder dataset with an accuracy of 91.77%

A Vision Transformer (ViT) model trained on the MS1MV3 dataset, specifically designed for face recognition tasks.

A liveness detection model based on the DINOv2 architecture, designed to distinguish real faces from spoofing attacks, achieving 99.32% accuracy on the evaluation dataset.

This model uses Google's ViT-base architecture to predict the presence of beards in facial images, achieving 100% accuracy on the test set.

A Vision Transformer (ViT) model trained on the MS1MV3 dataset, specifically designed for face recognition tasks.

This model is a face recognition model fine-tuned on an image folder dataset based on Google's ViT architecture, achieving near-perfect accuracy on the evaluation set.

This model is a face liveness detection model based on the Transformer library and has achieved excellent performance on the evaluation set.

An image classification model built on PyTorch and HuggingPics, specifically designed for face recognition tasks

Face recognition model based on keypoint relative position encoding, using ViT architecture and trained on the WebFace12M dataset

An advanced real-time system capable of accurately detecting and classifying subtle eye and eyebrow movements, including 'yes', 'no', and 'neutral' actions.

A fast race classification model based on YOLOv8/YOLO11 architecture, trained on the FairFace dataset for facial race classification tasks.

ConvNeXt-Nano face recognition model trained on the MS1MV3 dataset, compatible with the timm framework

A cartoon face recognition model fine-tuned based on microsoft/resnet-50, performing well in image classification tasks.

A cartoon face recognition model fine-tuned based on the Google Vision Transformer (ViT) architecture, excelling in image classification tasks

Sapiens is a vision Transformer model pretrained on 300 million high-resolution human images, focusing on human-centric vision tasks.

Deep face recognition model based on the ArcFace loss function, trained on the WebFace4M dataset using the IR101 architecture

RetinaFace is a lightweight face detection model based on MobileNet or ResNet50 backbone networks, capable of efficiently locating faces and providing facial keypoint coordinates.

This model is a facial expression classifier fine-tuned on the FER2013 dataset based on Google's ViT base model, capable of classifying images into four facial expressions.

Facial expression recognition model fine-tuned based on ResNet-50 architecture, trained on FER2013 and CKPlus datasets

A facial landmark detection model for Unity Sentis, capable of detecting 468 3D facial landmarks for applications like mesh deformation control.

Sapiens-2B is a Vision Transformer model pre-trained on 300 million high-resolution human images, specifically designed for human-centric vision tasks with exceptional generalization capabilities.

A Vision Transformer (ViT) model trained on the MS1MV3 dataset, specifically designed for face recognition tasks

Facial expression recognition model fine-tuned on the FER2013 dataset based on Microsoft's BEiT model

A vision model fine-tuned from google/vit-base-patch16-224-in21k for facial expression recognition tasks