Face detection and facial recognition are some of the major research areas in this century. Specially the recent developments of Artificial intelligence have started a revolutionary era of object detection technologies.
What is RetinaFace:
Simply, the Retina face is a high precision face detection algorithm model. It was developed very recently in 2019 by the Imperial College London in collaboration with InsightFace. The model calculates the bounding boxes for faces and identifies keypoints for the eyes and mouth. It operates effectively on high-resolution images without the need for resizing and employs hierarchical detection methods, enabling the reliable identification of small faces within the image.
RetinaFace facilitates the identification of small faces by employing hierarchical processing with a feature pyramid. It utilizes ResNet50 as its foundational architecture, providing feature vectors from various layers of ResNet50 to the detection phase.
RetinaFace can manage 3 different tasks. They are:
- Face detection
- 2D face allignment
- 3D face reconstruction
Architecture:
Let us examine the diagram below, which illustrates the complete pipeline of Retinaface. Initially, we have the feature pyramid network (FPN), followed by the cascade module, and concluding with the multitask loss.
The FPN is responsible for extracting features at five distinct levels from the two-dimensional image. The first four feature maps are derived using a pretrained ResNet model. The smallest feature map, located at the top, is obtained through a convolution operation with a 3×3 kernel and a stride of 2. Attached to this is the context module head, which employs five different filters to extract additional contextual information from the features. Ultimately, the resulting feature map is forwarded to the multi-level loss function.
Retina Face and Arc Face:
ArcFace is an advanced facial recognition algorithm designed to extract high-quality features from facial images. Renowned for its remarkable accuracy and resilience, it serves as an excellent option for applications necessitating precise facial recognition capabilities. ArcFace is particularly effective in situations where both high accuracy and minimal computational demands are critical, rendering it a favored choice for developers aiming to incorporate facial recognition seamlessly into their applications.
RetinaFace, conversely, is an advanced algorithm for face detection and alignment that demonstrates exceptional proficiency in identifying and aligning faces within images. It is meticulously crafted to address intricate situations, including the detection of faces across various scales and orientations. The algorithm’s primary advantage is its remarkable accuracy in detecting faces under challenging circumstances, rendering it an indispensable resource for applications necessitating reliable face detection functionalities.
pip install retinafacefrom retinaface import RetinaFaceRetina face is mainly used as a library package in python programming language.
This GitHub repository contains the basic introduction and using methods for retinaface.
Followings are some reference articles on retinaface:
https://ieeexplore.ieee.org/document/9589577/
https://medium.com/pythons-gurus/what-is-the-best-face-detector-ab650d8c1225