ailia Tech BLOG
HOPE-Net : A Machine Learning Model for Estimating Face Orientation
This is an introduction to「HOPE-Net」, a machine learning model that can be used with ailia SDK. You can easily use this model to create AI applications using ailia SDK as well as many other ready-to-use ailia MODELS.
Overview
HOPE-Net is a machine learning model released in October 2017 which compute the angles in three axes (yaw, pitch, and roll) of a face in an input image.
Detects even the most difficult face images (Source: https://github.com/natanielruiz/deep-head-pose)
Architecture
Face orientation detection is an important technology used in gaze detection and recognition of which objects is being watched in a scene.
Face orientation detection usually works by detecting key points of the target face and converting those points from 2D to 3D using a standard head model. However, there is a problem that the result depends on the accuracy of the face key points, and the need for ad-hoc fitting.
HOPE-Net uses multi-loss convolutional neural networks to detect the orientation of faces in a single shot. Using the face detected by the face detector as input, ResNet50 extracts features and FC Layer calculates yaw, pitch, and roll.
Source: https://arxiv.org/pdf/1710.00925
HOPE-Net performs best on AFLW2000, a dataset made of the first 2000 images of the Annotated Facial Landmarks in the Wild (AFLW) dataset, which have been re-annotated with 68 3D landmarks.
Source: https://arxiv.org/pdf/1710.00925
Usage
Use the following command to run HOPE-Net and detect face orientation from a web camera.
$ python3 hopenet.py -v 0
You can also use a faster version that uses ShuffleNetV2 instead of ResNet50 with the following command.
$ python3 blazehand.py --lite -v 0
Here is the kind of result you can expect.
Related topic
ailia Inc. has developed ailia SDK, which enables cross-platform, GPU-based rapid inference.
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