Cross-Model Deep Feature Fusion for Face Detection

Abstract

Face detectors using the single shot multibox detector have a small number of discontinuous fixed anchor scales that cannot fully match continuous faces, and these may degrade the accuracy when detecting tiny faces that are not fully matched. To address this issue, we utilize the capabilities and complementary features of the heterogeneous submodel to help the main model extract more abundant features and scale-invariant features from the heterogeneous submodel. First, we propose a deep feature fusion model of the heterogeneous convolutional neural networks to refine and integrate different levels of semantics and obtain a semantically enhanced feature pyramid. We then present a composite anchor loss function that trains the generated network end-to-end, which solves the accuracy-diversity tradeoff of these hybrid submodels. Finally, we apply the proposed approach to the a benchmark for face detection in unconstrained settings (FDDB) and WIDER FACE benchmarks and present the results using their respective standard evaluation protocols. Experiments result in our method surpassing the existing single-shot face detector and further improves the average accuracy of the original model.