Efficient Verification-Based Face Identification
2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)(2024)
Abstract
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.
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Key words
Face Identity,Neural Network,Face Recognition,Edge Devices,Convolutional Neural Network,Deep Neural Network,Batch Size,Additional Costs,Single Image,Training Phase,Multiple Images,Neural Architecture,Face Images,Batch Of Samples,Embedding Dimension,Embedding Vectors,Person Image,Query Image,Inference Phase,Verification Task,Registration Phase,Discriminative Feature Learning,V2 Dataset,Face Recognition Model,Linear Layer,Binary Classification,Recognition Model
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