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 )
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Studies show that neural networks, not unlike traditional programs, are subject to bugs, e. g., adversarial samples that cause classification errors and discriminatory instances that demonstrate the lack of fairness.
However, the lack of robustness in deep CNNs to adversarial examples has raised security concerns to enormous face recognition applications.
We identify that they, including those related to the inserted triggers, contain both content (semantic information) and style (texture information), which are recognized as a whole by DNNs at testing time.
After a gentle introduction to the general topic of biometric quality and a review of past efforts in face quality metrics, in the present work, we address the need for better face quality metrics by developing FaceQnet.
General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research.
Face recognition in the unconstrained environment is an ongoing research challenge.
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.