Efficient Verification-Based Face Identification
This work addresses the need for compact and computationally efficient face identification models for deployment on edge devices, representing an incremental improvement in efficiency.
The paper tackles the problem of efficient face verification by simplifying it from an embedding nearest neighbor search to a binary problem using personalized neural networks generated via a hypernetwork, resulting in a model with only 23k parameters and 5M FLOPS that matches or exceeds state-of-the-art performance on six datasets.
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.