Are you searching for high-quality, free facial recognition datasets to supercharge your AI projects? Look no further! We’ve curated the ultimate list of 19 top-notch, free image datasets specifically designed for facial recognition tasks. Whether you’re developing cutting-edge AI algorithms, fine-tuning machine learning models, or exploring computer vision applications, these datasets are your ticket to success. From celebrity faces to diverse age groups, and from multi-pose collections to emotion recognition challenges, our comprehensive guide covers it all. Dive into this treasure trove of facial data and elevate your facial recognition systems without spending a dime. Ready to revolutionize your computer vision projects? Let’s explore these game-changing free facial recognition datasets that will take your AI to the next level!.
19 Free Facial Datasets for Face Recognition Model Training
A facial recognition system can perform its computer vision tasks only when trained on quality image datasets. Without a quality image recognition dataset, you might not be able to develop a robust facial recognition system. But we have a solution.
Explore a repository of high-quality open-image datasets that can be accessed for free.
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Labeled Faces in the Wild (Link)
Another free-to-download large facial image dataset, Labeled Faces in the Wild, contains approximately 13,000 facial photographs specifically designed for performing unconstrained facial recognition tasks. The images are collected from the web and are labeled with the person’s name.
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CelebFaces (Link)
CelebFaces is a freely available image dataset containing face attribute images of more than 200,000 celebrities. Each of these images comes annotated with 40 attributes. Moreover, the annotations also include 10,000 and more identities and landmark localization. It was developed by MMLAB for non-commercial research purposes and face detection, localization, and attribute recognition.
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Tufts Face Database (Link)
Tufts Face database is a large-scale heterogeneous face detection database with various image modalities including photographic images, computerized sketches of faces, and 3D, thermal and infrared images of participants. This comprehensive collection of over 10,000 images has participants of both genders, a wide age range, and from different countries.
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Google Facial Expression Comparison (Link)
Google Facial Expression comparison is another large-scale free dataset containing face image triplets. Humans further annotate the images to specify which pair among the three have the most similar facial expression.
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UMDFaces (Link)
One of the largest datasets, UMDFaces features more than 367,000 annotated faces across 8,200 subjects. The database also contains more than 3.7 million annotated frames from videos using facial key points of 3,100 subjects.
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Face Images with Marked Landmark Points (Link)
This free facial recognition dataset has 7049 images, each marked with up to 15 keypoints. The keypoints per image can vary, but 15 is the maximum. All keypoint data is provided in a CSV file.
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UTKFace (Link)
The UTK Face dataset has 20,000 images of people of all ages. It includes information on age, ethnicity, and gender.
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MORPH (Link)
MORPH is a dataset for estimating age from faces. It has 55,134 images of 13,617 people aged 16 to 77.
YouTube with Facial Keypoints (Link)
YouTube With Facial Keypoints contains the facial images of celebrities taken from public forums. The images are cropped from videos and focused on facial key points across each frame.
Wider Face (Link)
Wider Face has more than 10,000 images of singles and groups of people. The dataset is grouped based on numerous scenes, such as parades, traffic, parties, meetings, etc.
Yale Face Database (Link)
The Yale Face Database has 165 images of 15 subjects under different lighting, expression, emotions, and environmental conditions.
Simpsons Faces (Link)
The Simpsons faces is a collection of images taken from the longest-running TV program, Simpsons, seasons 25 to 28. As the name suggests, this dataset contains 10,000 cropped images of the character faces appearing in the Simpsons show.
Real and Fake Face Detection (Link)
The Real and Fake face detection dataset is designed to help facial recognition systems better distinguish between real and fake facial images. The dataset contains more than 1000 real and 900 fake faces with varying recognizable difficulty.
Flickr Faces (Link)
Flickr Faces is a facial image dataset crawled from Flickr. The high-quality dataset features over 70,000 PNG images of people with distinct features such as age, nationality, ethnicity, and image background.
VGG Face (Link)
The VGG Face dataset has over 2.6 million images of 2,622 people for face identity recognition.
Multi-pose and Multi-expression Face Data (Link)
This dataset has 102,476 images of 1,507 Asians (762 males, 745 females). Each person has 62 multi-pose and 6 multi-expression images. The dataset includes various angles, poses, and lighting conditions. It’s useful for face and facial expression recognition.
Living Face & Anti-Spoofing Data (Link)
This dataset has anti-spoofing data for 1,056 people. It includes images from both indoor and outdoor scenes and covers all ages, with a focus on young and middle-aged people. The data includes multiple postures and expressions, useful for tasks like face payment and mobile phone unlocking.
Multi-Attribute Labelled Faces (MALF) Dataset (Link)
The Multi-Attribute Labelled Faces dataset has 5,250 images with 11,931 labelled faces. It supports detailed analysis of face detection in the wild and was introduced in 2015.
Google Facial Expression Comparison Dataset (Link)
The Google Facial Expression Comparison dataset has over 156k images and 500k triplets. Created by Google researchers, it focuses on analyzing facial expressions, such as emotion classification. It was published in 2018.
Since data drives AI and Computer Vision, you need high-quality data to develop a winning facial recognition system. This free-to-use and annotated image datasets can further your development goals. However, if you require highly-customized and accurately annotated image datasets, Shaip is the only solution.
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