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 )
Deep leaning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis and many others.
Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios.
To capture the underlying structure of live faces data in latent representation space, we propose to train the live face data only, with a convolutional Encoder-Decoder network acting as a Generator.
In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch.
After that, we apply the best norm-scaling setup in combination with various margins and conduct neural language models rescoring experiments in automatic speech recognition.
In recent years, few-shot learning problems have received a lot of attention.
In this work, we investigate the demographic bias of deep learning models in face recognition, age estimation, gender recognition and kinship verification.
Artificial Neural Networks (NN) are widely used for solving complex problems from medical diagnostics to face recognition.
Nowadays face recognition and more generally, image recognition have many applications in the modern world and are widely used in our daily tasks.
A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks.