Facial recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.
The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.
( Image credit: WIDER Face )
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks.
The neuron-wise discriminant criterion makes the input feature of each neuron in the output layer discriminative by introducing the discriminant criterion to each of the features.
The numbers of subjects and images acquired in web-scraped datasets are usually very large, with number of images on the millions scale.
Face quality assessment aims at estimating the utility of a face image for the purpose of recognition.
As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability.
Face image quality is an important factor to enable high performance face recognition systems.
SOTA for Face Quality Assessement on LFW
Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing.
This work considers the problem of domain shift in person re-identification. Being trained on one dataset, a re-identification model usually performs much worse on unseen data.
#3 best model for Person Re-Identification on MSMT17