Facial Keypoint Detection Dataset

Dataset comprises 5,000+ close-up images of human faces captured against various backgrounds. It is designed to facilitate keypoints detection and improve the accuracy of facial recognition systems. The dataset includes either presumed or accurately defined keypoint positions, allowing for comprehensive analysis and training of deep learning models. By utilizing this dataset, practitioners can explore various applications in computer vision… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/facial-keypoint-detection.

Bored with using the models that detect facial landmarks????

Interested in developing your own model that could identify landmarks on a face????

Then you are in the right place. This dataset will definitely help you fulfill your task. Detecting facial keypoints is a very challenging problem. Facial features vary greatly from one individual to another, and even for a single individual, there is a large amount of variation due to 3D pose, size, position, viewing angle, and illumination conditions. Computer vision research has come a long way in addressing these difficulties, but there remain many opportunities for improvement.

Each keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. There are 15 keypoints, which represent the following elements of the face:

left_eye_center, right_eye_center, left_eye_inner_corner, left_eye_outer_corner, right_eye_inner_corner, right_eye_outer_corner, left_eyebrow_inner_end, left_eyebrow_outer_end, right_eyebrow_inner_end, right_eyebrow_outer_end, nose_tip, mouth_left_corner, mouth_right_corner, mouth_center_top_lip, mouth_center_bottom_lip

Left and right here refer to the point of view of the subject.

The link between the CSV and the images is the index. To find out the corresponding image for a record in training.csv, search for index.jpg in the training folder.

Dataset

This dataset was created by Tom Nguyen

Contents

Facial Keypoints

## Overview

Facial Keypoints is a dataset for computer vision tasks - it contains Eye annotations for 759 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Parth Gupta

Released under Data files © Original Authors

Contents

The dataset is designed for computer vision and machine learning tasks involving the identification and analysis of key points on a human face. The facial keypoint detection system takes in any image with faces and predicts the location of 10 distinguishing keypoints on each face. The facial keypoints dataset used to train, validate and test the model consists of 3500 color images from the YouTube Faces Dataset. Examples of these keypoints are displayed below.

Original Dataset - https://www.kaggle.com/datasets/julianlenkiewicz/facialkeypoints68dataset

Dataset Creation Script - https://www.kaggle.com/code/sudhanshu2198/keypoints-detection-dataset

Trained Model Weights - https://www.kaggle.com/models/sudhanshu2198/facial-keypoint-detection-model

The Facial Keypoint Detection Dataset is designed to locate facial landmarks such as eyes, nose, mouth, and jawline. It supports applications like facial recognition, emotion analysis, augmented reality, and medical diagnostics using advanced computer vision and deep learning techniques.

Facial and key point detection in dairy cows can assist farms in building recognition systems and estimating cow facial postures. This dataset was primarily collected in Lu'an, Anhui Province, and Huai'an, Jiangsu Province.This dataset contains 2,538 images of Holstein cow faces under various conditions, including different lighting, occlusions, levels of blurriness, angles, as well as flipping, noise, and both single and multiple cows.The Labelme software was used to annotate the cow's facial detection bounding box and five key points including the left and right eyes, nose, and the corners of the mouth, which helps to advance the development of cow facial detection and pose estimation.

Facial Keypoints - Face Recognition Dataset

The dataset is designed for computer vision and machine learning tasks involving the identification and analysis of key points on a human face. It consists of images of human faces, each accompanied by key point annotations in XML format.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F3d7bd72ae7143ee767c2ec54aabde499%2Fimage_keypoint.png?generation=1683577579318981&alt=media" alt="">

Data Format

Each image from FKP folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the key points. For each point, the x and y coordinates are provided, and there is a Presumed_Location attribute, indicating whether the point is presumed or accurately defined.

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fb68d405e08a0d5dc6e5c87476758164d%2Fcarbon.png?generation=1684338809077422&alt=media" alt="">

Labeled Keypoints

1. Left eye, the closest point to the nose

2. Left eye, pupil's center

3. Left eye, the closest point to the left ear

4. Right eye, the closest point to the nose

5. Right eye, pupil's center

6. Right eye, the closest point to the right ear

7. Left eyebrow, the closest point to the nose

8. Left eyebrow, the closest point to the left ear

9. Right eyebrow, the closest point to the nose

10. Right eyebrow, the closest point to the right ear

11. Nose, center

12. Mouth, left corner point

13. Mouth, right corner point

14. Mouth, the highest point in the middle

15. Mouth, the lowest point in the middle

Keypoint annotation is made in accordance with your requirements.

🧩 This is just an example of the data. Leave a request here to learn more

🚀 You can learn more about our high-quality unique datasets here

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Face Landmark Detection

## Overview

Face Landmark Detection is a dataset for computer vision tasks - it contains Face annotations for 852 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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.

Dataset

This dataset was created by koustubhk

Released under CC0: Public Domain

Contents

Dataset for Facial Keypoints Detection

The development of facial expression recognition (FER) and facial expression generation (FEG) systems is essential to enhance human-robot interactions (HRI). The facial action coding system is widely used in FER and FEG tasks, as it offers a framework to relate the action of facial muscles and the resulting facial motions to the execution of facial expressions. However, most FER and FEG studies are based on measuring and analyzing facial motions, leaving the facial muscle component relatively unexplored. This study introduces a novel framework using surface electromyography (sEMG) signals from facial muscles to recognize facial expressions and estimate the displacement of facial keypoints during the execution of the expressions. For the facial expression recognition task, we studied the coordination patterns of seven muscles, expressed as three muscle synergies extracted through non-negative matrix factorization, during the execution of six basic facial expressions. Muscle synergies are groups of muscles that show coordinated patterns of activity, as measured by their sEMG signals, and are hypothesized to form the building blocks of human motor control. We then trained two classifiers for the facial expressions based on extracted features from the sEMG signals and the synergy activation coefficients of the extracted muscle synergies, respectively. The accuracy of both classifiers outperformed other systems that use sEMG to classify facial expressions, although the synergy-based classifier performed marginally worse than the sEMG-based one (classification accuracy: synergy-based 97.4%, sEMG-based 99.2%). However, the extracted muscle synergies revealed common coordination patterns between different facial expressions, allowing a low-dimensional quantitative visualization of the muscle control strategies involved in human facial expression generation. We also developed a skin-musculoskeletal model enhanced by linear regression (SMSM-LRM) to estimate the displacement of facial keypoints during the execution of a facial expression based on sEMG signals. Our proposed approach achieved a relatively high fidelity in estimating these displacements (NRMSE 0.067). We propose that the identified muscle synergies could be used in combination with the SMSM-LRM model to generate motor commands and trajectories for desired facial displacements, potentially enabling the generation of more natural facial expressions in social robotics and virtual reality.

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.

Explore the nuances of facial analysis and witness the precision unleashed through the meticulous annotation of keypoints, paving the way for innovations in biometrics, emotion recognition, and beyond. Embark on a journey into the realm of facial recognition with the Facial Detection Keypoints dataset. This comprehensive resource unveils the intricate details of facial features, providing a roadmap for accurate detection and keypoint localization.

The Cat and Dog Facial Landmarks Dataset contains 80,000 annotated images across a wide range of indoor and outdoor environments. The data diversity includes multiple scenes, multiple dog and cat breeds, different face angles, different shooting distances. Each image is annotated with facial landmarks (keypoints). This dataset can be used for tasks such as cat and dog face recognition, breed classification, pet identity authentication, biometric verification, and animal face detection

abinazmi/rugby-pitch-keypoints-detection dataset hosted on Hugging Face and contributed by the HF Datasets community

Keypoint Detection AI Market Outlook

As per our latest research, the global Keypoint Detection AI market size in 2024 stands at USD 1.92 billion, with a robust compound annual growth rate (CAGR) of 22.7% anticipated from 2025 to 2033. By the end of 2033, the market is projected to reach USD 13.17 billion, driven by surging adoption across diverse industries and continuous advancements in deep learning and computer vision technologies. The increasing demand for intelligent automation, enhanced security systems, and real-time analytics is fueling the rapid expansion of the Keypoint Detection AI market worldwide.

A primary growth factor propelling the Keypoint Detection AI market is the exponential rise in computer vision applications, particularly in sectors such as healthcare, automotive, and robotics. In healthcare, keypoint detection AI is revolutionizing diagnostics and treatment planning by enabling precise identification of anatomical landmarks in medical imaging. This technology is instrumental in automating complex tasks such as organ segmentation, movement analysis, and surgical navigation, leading to improved patient outcomes and operational efficiency. The proliferation of AI-powered medical devices and the integration of keypoint detection with telemedicine platforms are further accelerating market growth, as healthcare providers seek to leverage advanced analytics for enhanced decision-making and patient monitoring.

Another significant driver is the automotive industry’s increasing reliance on keypoint detection AI for advanced driver-assistance systems (ADAS) and autonomous vehicles. The technology enables real-time detection and tracking of objects, pedestrians, and lane markings, thereby enhancing vehicle safety and navigation. With the global push towards autonomous mobility and intelligent transportation systems, automotive manufacturers are investing heavily in AI-powered perception systems that utilize keypoint detection for robust environmental understanding. This trend is complemented by the growing adoption of robotics in manufacturing and logistics, where keypoint detection AI facilitates precise object manipulation, quality inspection, and human-robot collaboration, boosting productivity and reducing operational costs.

Furthermore, the surge in demand for intelligent security and surveillance solutions is catalyzing the expansion of the Keypoint Detection AI market. Security agencies and enterprises are increasingly deploying AI-driven surveillance systems that leverage keypoint detection for facial recognition, behavior analysis, and anomaly detection in real-time. These systems offer superior accuracy and scalability compared to traditional methods, making them indispensable for critical infrastructure protection, urban safety, and retail loss prevention. The integration of keypoint detection with IoT devices and cloud-based analytics platforms is also enabling seamless monitoring and rapid response to security threats, thereby reinforcing the market’s upward trajectory.

From a regional perspective, North America currently dominates the Keypoint Detection AI market, accounting for over 38% of the global revenue in 2024. The region’s leadership is attributed to the presence of leading technology firms, high R&D investments, and early adoption across key industries. Europe follows closely, driven by stringent regulatory frameworks for safety and security, as well as robust automotive and healthcare sectors. The Asia Pacific region is witnessing the fastest growth, with a projected CAGR of 25.4% through 2033, fueled by rapid industrialization, expanding digital infrastructure, and government initiatives supporting AI innovation. Latin America and the Middle East & Africa are also emerging as promising markets, supported by increasing investments in smart city projects and digital transformation initiatives.

Component Analysis

The Keypoint Detection AI market is segmented by component into software, hardware, and services, each playing a pivotal role in the ecosystem. The software segment currently leads the market, accounting for approximately 52% of the total revenue in 2024. This dominance is attributed to the proliferation of advanced AI algorithms, deep learning libraries, and open-source frameworks that facilitate the development and deployment of keypoint detection models. Software solutions are cont

Keypoint Detection AI Market Outlook

As per our latest research, the global Keypoint Detection AI market size reached USD 1.68 billion in 2024, reflecting robust adoption across industries. The market is poised to expand at a CAGR of 22.4% during the forecast period, with projections indicating a market value of USD 12.1 billion by 2033. This remarkable growth trajectory is primarily driven by escalating demand for intelligent automation, advancements in deep learning algorithms, and the increasing integration of computer vision technologies across diverse sectors.

One of the fundamental growth drivers of the Keypoint Detection AI market is the rapid advancement in artificial intelligence and machine learning, particularly in the realm of computer vision. As industries increasingly digitalize their operations, the need for precise object tracking, real-time motion analysis, and enhanced image recognition becomes paramount. Keypoint detection enables machines to identify and track critical points within images or videos, facilitating applications from facial recognition in security systems to gesture analysis in human-computer interaction. The proliferation of high-resolution cameras and IoT devices has further amplified the volume and complexity of visual data, necessitating sophisticated AI models to extract actionable insights. As organizations seek to automate workflows and enhance operational efficiency, the adoption of keypoint detection AI solutions is expected to accelerate.

Another significant factor propelling the market is the surge in demand from healthcare, automotive, and robotics sectors. In healthcare, keypoint detection AI is revolutionizing diagnostics by enabling precise anatomical landmark identification, aiding in surgical planning, and enhancing patient monitoring. In the automotive industry, the technology is integral to advanced driver-assistance systems (ADAS) and autonomous vehicles, where real-time object and pedestrian detection are critical for safety. Robotics applications leverage keypoint detection for improved manipulation, navigation, and human-robot collaboration. The convergence of AI with edge computing is further enabling real-time processing of visual data, making these solutions more accessible and scalable across industries. This cross-sectoral adoption is expected to be a major catalyst for sustained market growth.

The third pivotal growth factor is the increasing investments by both public and private sectors in AI research and development. Governments worldwide are launching initiatives to foster AI innovation, while venture capital funding for computer vision startups continues to surge. This influx of capital is fueling the development of more accurate, efficient, and versatile keypoint detection models. Additionally, the open-source movement within the AI community has democratized access to advanced algorithms and frameworks, enabling even small and medium enterprises to experiment with and deploy keypoint detection solutions. As the ecosystem matures, collaboration between academia, industry, and government entities is expected to accelerate breakthroughs, further propelling the market forward.

Edge Visual-Servoing AI is emerging as a transformative technology within the Keypoint Detection AI market, particularly in applications requiring precise control and real-time feedback. This technology leverages edge computing to process visual data locally, reducing latency and enabling faster response times. In sectors such as robotics and autonomous vehicles, Edge Visual-Servoing AI enhances the ability to perform intricate tasks with high precision by dynamically adjusting to environmental changes. By integrating visual-servoing capabilities at the edge, industries can achieve more efficient and reliable automation, paving the way for advancements in fields like manufacturing, healthcare, and logistics. As the demand for real-time processing increases, Edge Visual-Servoing AI is set to play a crucial role in the evolution of intelligent systems.

From a regional perspective, North America remains the dominant market for Keypoint Detection AI, driven by early technology adoption, a strong presence of leading AI vendors, and substantial investments in R&D. However, Asia Pacific is rapidly emerging as a key growth region, fueled by expanding industrial automation, a burgeoning startup ecosystem, and increa

Off-the-shelf biometric data (human face) covers 3D depth, segmentation: face organs and accessory, key points, facial expression, alpha Matte, age in variety and etc. All the Biometric Data are collected with signed authorization agreement.