#embedding similarity

IMGNet introduces a face verification model that replaces cosine similarity with sliding window sign pattern matching, achieving 96.27% accuracy on LFW with a 10.58 MB model trained on CASIA-WebFace. This novel approach could enable more efficient and compact face verification systems, and the finding that sign pattern matching improves ArcFace embeddings without retraining suggests a fundamental property of well-trained face embeddings. The model uses a SW Block replacing standard convolution with multi-scale relational operations, an IMG Sign MSE Loss defined purely over sign pattern agreement, and a voting system combining three metrics. Applied to ArcFace (buffalol) without retraining, IMG Sign Score achieves 99.58% on LFW, only 0.24% below ArcFace+Cosine.