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Facial Landmarks Estimation

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Detect fiducial keypoints from an image of a face.



Use Case

Fiducial Landmarks


Transfer Learning Toolkit

Latest Version



November 24, 2021


2.24 MB

Facial Landmark Estimator (FPENet) Model Card

Model Overview

The FPENet model described in this card is a facial keypoints estimator network, which aims to predict the (x,y) location of keypoints for a given input face image. FPEnet is generally used in conjuction with a face detector and the output is commonly used for face alignment, head pose estimation, emotion detection, eye blink detection, gaze estimation, among others.

This model predicts 68, 80 or 104 keypoints for a given face- Chin: 1-17, Eyebrows: 18-27, Nose: 28-36, Eyes: 37-48, Mouth: 49-61, Inner Lips: 62-68, Pupil: 69-76, Ears: 77-80, additional eye landmarks: 81-104. It can also handle visible or occluded flag for each keypoint. An example of the kaypoints is shown as follows:

Model Architecture

This is a classification model with a Recombinator network backbone. Recombinator networks are a family of CNN architectures that are suited for fine grained pixel level predictions (as oppose to image level prediction like classification). The model recombines the layer inputs such that convolutional layers in the finer branches get inputs from both coarse and fine layers.

Training Algorithmn

This model was trained using the FPENet entrypoint in TAO. The training algorithm optimizes the network to minimize the manhattan distance (L1), squared euclidean (L2) or the Wing Loss over the keypoints. Individual face regions can be weighted based on- the 'eyes', the 'mouth', the 'pupil' and the rest of the 'face'.

Training Data and Ground-truth Labeling Guidelines

A pre-trained (trainable) model is available, trained on a combination of NVIDIA internal dataset and Multi-PIE dataset. NVIDIA internal data has approximately 500k images and Multipie has 750k images.

The ground truth dataset is created by labeling ground-truth facial keypoints by human labellers.

If you are looking to re-train with your own dataset, please follow the guideline below.

  • Label the keypoints in the correct order as accuractely as possible. The human labeler would be able to zoom in to a face region to correctly localize the keypoint.
  • For keypoints that are not easily distinguishable such as chin or nose, the best estimate should be made by the human labeler. Some keypoints are easily distinguishable such as mouth corners or eye corners.
  • Label a keypoint as "occluded" if the keypoint is not visible due to an external object or due to extreme head pose angles. A keypoint is considered occluded when the keypoint is in the image but not visible.
  • To reduce discrepency in labeling between multiple human labelers, the same keypoint ordering and instructions should be used across labelers. An independent human labeler may be used to test the quality of the annotated landmarks and potential corrections.

Face bounding boxes labeling:

  • Face bounding boxes should be as tight as possible.
  • Label each face bounding box with an occlusion level ranging from 0 to 9. 0 means the face is fully visible and 9 means the face is 90% or more occluded. For training, only faces with occlusion level 0-5 are considered.
  • The datasets consist of webcam images so truncation is rarely seen. If faces are at the edge of the frame with visibility less than 60% due to truncation, this image is dropped from the dataset.

The Sloth and Label-Studio tools have been utilized for labeling.


Evaluation Dataset

The evaluation is done on the Multi-PIE dataset Users IDs that are used for KPI- 342 079 164 250 343 080 165 251 344 081 166 252 345 082 167 253 346 083 168 254 084 169 255

Methodology and KPI accuracy

The region keypoint pixel error is the mean euclidean error in pixel location prediction as compared to the ground truth. We bucketize and average the error per face region (eyes, mouth, chin, etc.). Metric- Region keypoints pixel error

  • All keypoints: 6.1
  • Eyes region: 3.33
  • Mouth region: 2.96

Average latency

  • Batch Size = 1 at FP16
  • T4 - 0.33 ms
  • Jetson AGX - 0.84 ms
  • Jetson NX - 1.3 ms (all measurements using trtexec on the specific hardware)

Real-time Inference Performance

The inference uses FP16 precision. The inference performance runs with trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.

Device Precision Batch_size FPS
Nano FP16 1 115
NX FP16 1 483
Xavier FP16 1 1015
T4 FP16 1 2489

How to use this model

This model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream 6.0 or TensorRT.

There are two flavors of the model:

  • trainable
  • deployable

The trainable model is intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of TAO container can be used to re-train. The deployable model is intended for efficient deployment on the edge using DeepStream or TensorRT. The trainable and deployable models are encrypted and will only operate with the following key:

  • Model load key: nvidia_tlt

Please make sure to use this as the key for all TAO commands that require a model load key.


Images of 80 X 80 X 1


N X 2 keypoint locations. N X 1 keypoint confidence.

N is the number of keypoints. It can have a value of 68, 80, or 104.


Some known limitations include relative increase in keypoint estimation error in extreme head pose (yaw > 60 degree) and occlusions.

Model versions:

  • trainable_v1.0 - Pre-trained model that is intended for training.
  • deployable_v1.0 - Deployment models that is intended to run on the inference pipeline.
  • deployable_v3.0 - Deployment models that is intended to run on the inference pipeline with int8 calibration.



  • Honari, S., Molchanov, P., Tyree, S., Vincent, P., Pal, C., & Kautz, J. (2018). Improving landmark localization with semi-supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1546-1555).

  • Feng, Z. H., Kittler, J., Awais, M., Huber, P., & Wu, X. J. (2018). Wing loss for robust facial landmark localisation with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2235-2245).

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License to use this model is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses.

Ethical Considerations

Training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.