Facial & Body Part Datasets for AI/ML Training

Description: A specialized dataset of human facial images affected by lighting artifacts, also known as “ghost images,” with detailed annotations. Ideal for training and testing facial recognition models under challenging lighting conditions to improve accuracy and robustness.

Description: Group photos: (The goal of this data collection was to collect images of groups of people. A group is defined as 2-6 people)

Description: Historic Image Collections: Image datasets from Non-EU/ UK Region

Description: Image- Historical Facial Images (Unique participants/videos: 30)

Description: Historical photos and Videos: Collect 1 enrollment + 15 historical photos

Description: Image- Facial Images ( Indoors, Outdoors, Webcam) (Unique participants/videos: 10)

Description: A curated facial recognition verification dataset featuring diverse skin tones, with annotated images to support accurate identity verification across varied demographics. Ideal for training and evaluating AI models for fairness, inclusivity, and real-world robustness.

Description: Facial recognition datasets consist solely of images of faces, with no additional annotations. They include diverse examples of facial features, poses, and lighting conditions, and are used to train and evaluate facial recognition systems for tasks like face detection and recognition.

Recording Condition: Lighting Condition: - Bright Light Or Sunlight - Shade Or Overcast - Night Or Dim Light

Description: The "Glasses Segmentation Dataset" is aimed at the apparel and visual entertainment sectors, incorporating a diverse array of internet-collected images with resolutions from 165 x 126 to 1250 x 1458 pixels. This dataset focuses on semantic segmentation of various types of eyewear, including pure transparent glasses, sunglasses, and translucent glasses, providing detailed annotations for each category.

Description: The "Hair Semantic Segmentation Dataset" serves the apparel and media & entertainment industries, featuring a curated collection of internet-collected images with resolutions varying from 343 x 358 to 2316 x 3088 pixels. This dataset specializes in high-precision contour and semantic segmentation of hair, offering detailed annotations for a wide range of hairstyles and textures.

Description: The "Head and Neck Semantic Segmentation Dataset" is designed for the e-commerce & retail and media & entertainment sectors, featuring a collection of AI-generated cartoon images with resolutions above 1024 x 1024 pixels. This dataset focuses on semantic segmentation, specifically targeting the main character's head, including face, hair, and any accessories, as well as the neck area up to the collarbone, with an allowance for small, unsegmented parts on the edges.

Description: The "Human And Accessories Segmentation Dataset" is a valuable resource for the apparel, e-commerce, and media & entertainment industries, featuring internet-collected images with resolutions ranging from 584 x 429 to 3744 x 5616. This dataset is rich in diversity, encompassing a wide array of accessories like mobile phones, suitcases, skateboards, and animals, all annotated for semantic segmentation.

Description: The "Human Body High Precision Segmentation Dataset" is a comprehensive collection aimed at the apparel, e-commerce, and visual entertainment sectors, combining manually shot and internet-collected images with resolutions from 316 × 600 to 6601 × 9900. It focuses on high-precision segmentation of the human body, capturing intricate details of limbs, clothing, facial features, skin, and accessories.

Description: Images are from internet. Resolution ranges from 105 x 251 to 319 x 951.

Description: The "Portrait Matting Dataset" caters to the apparel and media & entertainment sectors, featuring a diverse collection of live screenshot images with resolutions varying from 138 × 189 to 6000 × 4000. This dataset is comprehensive, including single individuals, groups, and their accessories, and is annotated for contour, semantic, and instance segmentation tasks.

Description: The "Human Body Semantic Segmentation Dataset" serves the fashion, internet, and entertainment sectors with a diverse collection of human body images. This dataset, featuring an even distribution across genders and ages from various countries, is ideal for applications requiring detailed analysis of human postures, hairstyles, and different scenarios. With fine labeling of 19 human body areas, it facilitates advanced semantic segmentation tasks.

Description: The "Human Contour Segmentation And Keypoints Dataset" is aimed at the apparel and visual entertainment industries, featuring a collection of internet-collected images with resolutions ranging from 103 x 237 to 329 x 669 pixels. This dataset is focused on contour segmentation and key points annotation, covering comprehensive human body keypoints including facial features, limbs, and extremities, facilitating detailed human posture and movement analysis.

Description: Images are from internet. Resolution ranges from 1280 x 720 to 2048 x 1080.

Description: The "Indoor Facial 130 Expressions Dataset" is designed for applications in media & entertainment and mobile sectors, featuring a collection of internet-collected indoor facial images with resolutions ranging from 443 x 443 to 1127 x 1080 pixels. This dataset specializes in key points annotation, providing 130 key points for each facial expression, offering a detailed foundation for emotion recognition, facial animation, and interactive applications.

Description: The "Indoor Facial 182 Keypoints Dataset" is a specialized resource for the internet, media, entertainment, and mobile industries, focusing on detailed facial analysis. It includes images of 50 individuals in indoor settings, with a balanced gender distribution and ages ranging from 18 to 50. Each face is annotated with 182 key points, facilitating precise facial feature tracking and analysis.

Description: The "Indoor Facial 75 Expressions Dataset" enriches the internet, media, entertainment, and mobile sectors with an in-depth exploration of human emotions. It features 60 individuals in indoor settings, showcasing a balanced gender representation and varied postures, with 75 distinct facial expressions per person. This dataset is tagged with facial expression categories, making it an invaluable tool for emotion recognition and interactive applications.

Description: The "Glasses Segmentation Dataset" is aimed at the apparel and visual entertainment sectors, incorporating a diverse array of internet-collected images with resolutions from 165 x 126 to 1250 x 1458 pixels. This dataset focuses on semantic segmentation of various types of eyewear, including pure transparent glasses, sunglasses, and translucent glasses, providing detailed annotations for each category.

Description: The "Portrait Matting Dataset" caters to the apparel and media & entertainment sectors, featuring a diverse collection of live screenshot images with resolutions varying from 138 × 189 to 6000 × 4000. This dataset is comprehensive, including single individuals, groups, and their accessories, and is annotated for contour, semantic, and instance segmentation tasks.

Description: The "Pupils Segmentation Dataset" is tailored for applications in the beauty and media & entertainment industries, consisting of internet-collected images with resolutions varying from 90 x 89 to 419 x 419 pixels. This dataset focuses on semantic segmentation, providing subdivision annotations specifically for pupil locations to enhance detailed eye-related features in digital content.

Description: The "Segmentation and Key Points of Human Body Dataset" is designed for the apparel and visual entertainment sectors, featuring a collection of internet-collected images with resolutions ranging from 1280 x 960 to 5184 x 3456 pixels. This dataset is comprehensive, including instance and semantic segmentation of 27 categories of body parts along with 24 key points annotations, providing detailed data for human body analysis and applications.

Description: The "Shaven Head Segmentation Dataset" is designed for the media and entertainment industry, featuring a collection of internet-collected images with resolutions ranging from 1360 x 1656 to 2160 x 2702 pixels. This dataset specializes in semantic segmentation, providing annotations for various categories such as the background, obstructions, head, ears, and skin, focusing on individuals with shaven heads for detailed character modeling and digital content creation.

Description: Our "Single-person Portrait Matting Dataset" is a pivotal resource for the fashion, media, and social media industries, providing finely labeled portrait images that capture a wide range of postures and hairstyles from various countries. With a focus on high-resolution images exceeding 1080 x 1080 pixels, this dataset is tailored for applications requiring detailed segmentation, including hair, ears, fingers, and other intricate portrait features.

Description: The "Upper Eyelid Segmentation Dataset" is designed for the beauty and visual entertainment industries, incorporating internet-collected images with resolutions from 100 x 100 to 400 x 400 pixels. This focused dataset is dedicated to semantic segmentation of the upper eyelid, with annotations covering both eyes, facilitating detailed eye makeup applications and character modeling.