Landmark Annotation
& Keypoint Labeling Services

Landmark annotation involves marking precise keypoints, either (x, y) coordinates or 3D points, on objects, people, or environments within images and video. These reference points help AI models understand structure, position, and motion. It is widely used for facial analysis, pose estimation, gesture recognition, medical imaging, robotics, AR/VR, and autonomous navigation, wherever a bounding box isn't precise enough. Landmark annotation is one of the services we offer under our data labeling services.

Facial landmark annotation maps coordinates for the eyes, nose, lip corners, jawline and ears onto face images. These labels train biometric models to verify identities, track expressions, analyze gaze direction, and detect micro-expressions. Dense facial grids of 68 to 468 points provide the structural detail needed for accurate recognition, liveness detection, and emotion AI.

Landmark annotation covers facial keypoints, hand joints (21 points), full-body pose landmarks, object reference corners, road markers, anatomical points, and structural anchors. Supported use cases include AR/VR, sports biomechanics, robotics, medical imaging, autonomous vehicles, and any vision-based AI requiring coordinate-level precision across both static images and video sequences.

When landmarks are partially hidden or occluded, annotators use contextual visual cues, depth inference, and landmark interpolation to estimate and mark positions consistently. Occlusion flags are applied per client-defined taxonomy rules. This keeps labels usable in dense or partially obscured scenes.

Yes. We provide frame-by-frame keypoint tracking to maintain temporal consistency across video sequences. This is essential for sports action recognition, movement analysis, gesture-based HCI, and driver monitoring systems. Our annotators are trained in temporal continuity, so landmark placement stays consistent across frames even through occlusions and fast motion.

Landmark annotations are delivered in COCO JSON, CSV, HDF5, MediaPipe format, OpenPose JSON, DeepLabCut, or client-defined schemas. Outputs are structured for direct integration with training pipelines, evaluation frameworks, and dataset versioning tools. Delivery is available via AWS S3, GCP, Azure, or secure SFTP depending on your pipeline requirements.

We split large projects into batches and review each one at fixed checkpoints. With a 540+ in-house team, we scale to millions of keypoints per month. We update a shared guideline document whenever the taxonomy changes, so labels stay consistent as volume grows.

Our workflows are ISO 27001-aligned, HIPAA-aligned and GDPR-aligned. All annotators sign NDAs before any project access, roles are permission-scoped, and automated security audits run continuously across every project environment, whether the data is biometric, medical or general training data. You can read more about how we work on our about page.

Pricing depends on keypoint density, labeling complexity, volume and review requirements. We price per image, per keypoint, per hour or as a monthly retainer. Because we work from Pune, costs are typically 40–60% below US/UK rates, and every project starts with a free pilot batch. Our data labeling pricing guide has US and India benchmarks. Request a quote and we'll price it against your dataset.

Yes. Our annotators work within your internal tooling or any preferred third-party annotation platform — including Scale AI, Labelbox, CVAT, Roboflow, SuperAnnotate, V7, and others. We adapt to your stack rather than asking you to switch platforms.

Our 540+ in-house team members are trained for keypoint work rather than general labeling. Every batch goes through deviation checks, three-tier QA and expert review against our accuracy target. We have worked this way since 2017, work in all the major annotation platforms, and offer white-label capacity. Every project starts with a free pilot so you can check quality before committing.

Building an in-house landmark annotation team requires 8–12 weeks for hiring, training, and specialization — and fixes your capacity at the team size you can sustain. Outsourcing keypoint labeling to a specialist provider removes those bottlenecks: you get immediate access to a trained, dedicated team, a mature QC pipeline, and costs 40–60% below US/UK rates. For medical, biometric, or ADAS projects, our security framework also reduces the setup you would otherwise do yourself.

Pose and gesture keypoint annotation is used across sports analytics (player biomechanics and gait analysis), healthcare (physical therapy AI and surgical guidance), retail (AR virtual try-on and gesture-controlled shopping), security (touchless access), accessibility (sign language interpretation), ADAS (driver monitoring and fatigue detection), and entertainment (motion capture for gaming and VR).