In This Article
What Is Head Pose Estimation?
Head pose estimation is a specific task in computer vision that involves determining the orientation of a person’s head in 3D space, typically relative to the camera or other reference frame. This can be done using a variety of techniques, such as tracking facial landmarks or using deep learning algorithms to predict a subject’s head pose directly from an image or video.
Head pose estimation is used in a wide range of applications, including virtual reality, human-machine interaction, and surveillance. Additionally, it is used in areas like driver drowsiness detection, face recognition, and more.
This is part of a series of articles about body segmentation.
What Are the Use Cases for Head Pose Estimation?
Head pose estimation has a wide range of use cases, including:
- Facial recognition: Head pose estimation can be used to determine the position and orientation of a person’s face in an image, which can be used to improve the accuracy of facial recognition systems.
- Surveillance and security: Head pose estimation can be used to track the movement of people in videos, which can be used for applications such as security and monitoring.
- Human-computer interaction: Head pose estimation can be used to control devices or interfaces through gestures or movements, which can be used for applications such as gaming and virtual reality.
- Computer graphics: Head pose estimation can be used to create realistic animations of human movement, which can be used for applications such as video games, movies, and virtual reality.
- Augmented reality: Head pose estimation can be used to track the head’s orientation and position in real-time and to adjust the display of virtual objects accordingly, providing a more immersive experience.
- Driver monitoring: Head pose estimation can be used to detect and alert the driver if he/she is getting drowsy or distracted, which can help to prevent accidents.
- Human-robot interaction: Head pose estimation can be used to track the head’s orientation and position, to understand the focus of attention of the human, and to adjust the robot’s actions accordingly.
Two-Step Head Pose Estimation vs. End-to-End Hopenet Model
Let’s compare a traditional approach to head pose estimation, which first detects facial landmarks and then optimizes the algorithm for detecting headpose, to a newer approach that directly predicts head pitch from RGB images of humans.
Traditional Two-Step Approach
The traditional approach requires a preprocessing step that involves applying a face detection algorithm to the image to identify the regions of interest, in this case human faces. It may also require a calibrated camera with known intrinsic parameters (e.g. focal length, optical center, radial distortion). It also requires a preprocessing step that involves applying a face detection algorithm to the image to identify the regions of interest, in this case human faces. This step is important for identifying face regions cropped out of the image.
This head pose estimation approach can be split into two stages:
Step 1: Detecting facial landmarks
This step establishes the relationship between the facial landmarks in a 2D image and their positions in a 3D model. These facial landmarks include eyes, noses, eyebrows, lips, chins, and more. Aligning these landmarks is necessary for the next stage (the optimization).
To establish the correspondence between the 2D image and 3D facial model:
- Use a 2D facial landmark tracker like Facial Alignment Network (FAN) or Dlib to identify the landmarks in the image and produce their coordinates.
- Fit a 3D model of a human face to match these points to their 3D equivalents. A common approach is to fit a simple model of the mean human face.
Step 2: Optimize the algorithm for head pose estimation
This step involves optimizing the algorithm for head pose parameters.
It is possible to describe the movement of 3D rigid objects using translation and rotation vectors. This requires identifying the rotation matrix (R) and translation vector (T) that define the mapping between the real-world and camera-based coordinates – known as the perspective-n-point problem.
One way to address this problem is to use OpenCV functions such as solvePnP and solvePnPRansac. The optimization process should produce the necessary head pose parameters.
When using this approach, the model’s performance is highly sensitive to facial landmark prediction errors. It relies on the selected head model and subset of points used to align the head model.
End-to-End Approach: The Hopenet Model
To avoid the problems of the previous approach, the authors of the Hopenet model directly predict the Euler angles (the pitch, roll, and yaw) from RGB images of human heads. It uses a deep neural network to detect head poses more quickly and accurately.
This model’s architecture includes three neural network branches, with each branch responsible for different Euler angles. The branches connect to a backbone network with fully-connected layers specific to each branch.
Hopenet is trained with three losses(one for each branch). Each loss is a linear combination of cross-entropy loss and mean-squared error loss. The use of the mean-squared error allows more accurate fine-grained predictions.
Head Pose Estimation Datasets
The BIWI Head Pose Dataset is a dataset of head poses that was created for the purpose of training and evaluating algorithms for head pose estimation. The dataset contains a large number of images of people’s heads taken from a variety of angles and under different lighting conditions.
The dataset includes images taken with a static camera, as well as images taken from a moving camera, and includes both indoor and outdoor scenes. The dataset provides ground truth head pose information, typically in the form of yaw, pitch, roll angles. The dataset was created by the Computer Vision Laboratory of the ETH Zurich and the Institute of Computer Science of the University of Basel.
The Annotated Facial Landmarks in the Wild (AFLW) dataset is a publicly available dataset of images and corresponding facial landmarks annotations. The dataset contains more than 25,000 images of faces, each with up to 21 facial landmarks with annotations. The images in the dataset were collected “in the wild”, meaning that they were not taken in controlled conditions, and they include a wide range of variations in pose, expression, occlusion, and lighting.
The AFLW dataset is commonly used in head pose estimation, facial landmark detection, and facial alignment tasks. The data can be used for training and testing of various algorithms, and it is particularly useful for evaluating the robustness of facial landmark detection methods under challenging conditions. This dataset provides a benchmark for the performance of facial landmark detection algorithms and it is widely used in the research community.
The Wider Facial Landmarks in the Wild (WFLW) dataset is a large-scale facial landmarks dataset that contains images of faces in various poses and expressions. Each face also comes annotated with 98 facial landmarks. It was introduced in 2018 by researchers at Nanjing University of Science and Technology. The dataset is designed to be challenging, containing faces in a wide range of poses and expressions, as well as under various occlusion and lighting conditions. It contains 10,000 images divided into a testing set and a training set. The dataset is widely used to create benchmarks for facial landmark detection and head pose estimation models.
The Caltech Occluded Faces in the Wild dataset is a set of images and videos of faces that have been occluded by various objects such as hands, hats, and glasses. The dataset is designed to evaluate the performance of face detection and recognition algorithms in the presence of occlusions, which can make it more difficult for the algorithm to correctly identify a person. The dataset includes a total of 1,345 images with a wide range of occlusion types and levels of difficulty. This dataset is commonly used to evaluate the robustness of face recognition algorithms under occlusion.
The Head-Pose Detection dataset is a set of images generated with IEE-Simulator and Blender using various head poses. Each image is labeled based on nine poses: straight, turned left, turned bottom-left, turned top-left, turned right, turned bottom-right, turned top-right, looking up, and reclined. It includes over 16,000 training images and another 2,800 testing images, in addition to 4,700 images for improvements.