Abstract

The first layer of a deep CNN backbone applies filters to an image to extract the basic features available to later layers. During training, some filters may go inactive, meaning all weights in the filter are near zero. An inactive filter in the final model represents a missed opportunity to extract a useful feature. This phenomenon is especially prevalent in specialized CNNs such as for face recognition (as opposed to, e.g., ImageNet). For example, in one of the most widely-used face recognition models (ArcFace), about half of the filters in the first layer are inactive. We propose a novel approach designed and tested specifically for face recognition networks, known as 'CRAFT: Contextual Re-Activation of Filters for Face Recognition Training'. Additionally, CRAFT achieves statistically significant improvements in accuracy over standard training on face recognition benchmarks such as AgeDB-30, CPLFW, LFW, CALFW, CFP-FP, IJBB, and IJBC where accuracy has largely saturated. Notable improvements are observed, with significant gains on the highly challenging Hadrian and Eclipse datasets. © 2025 IEEE.