Efficient Verification-Based Face Identification

We study the problem of performing face verification with an efficient neural model
$f$
. The efficiency of
$f$
stems from simplifying the face verification problem from an embedding nearest neighbor search into a binary problem; each user has its own neural network
$f$
. To allow information sharing between different individuals in the training set, we do not train
$f$
directly but instead generate the model weights using a hypernetwork
$h$
. This leads to the generation of a compact personalized model for face identification that can be deployed on edge devices. Key to the method's success is a novel way of generating hard negatives and carefully scheduling the training objectives. Our model leads to a substantially small
$f$
requiring only 23k parameters and 5M floating point operations (FLOPS). We use six face verification datasets to demonstrate that our method is on par or better than state-of-the-art models, with a significantly reduced number of parameters and computational burden. Furthermore, we perform an extensive ablation study to demonstrate the importance of each element in our method.