Nested Shallow CNN-Cascade for Face Detection in the Wild

Face detection in the wild is a challenging vision problem due to large variations and unpredictable ambiguities commonly existed in real world images. Whilst introducing powerful but complex models is often computationally inefficient, using hand-crafted features is hence problematic. In this paper, we propose a nested CNN-cascade learning algorithm that adopts shallow neural network architectures that allow efficient and progressive elimination of negative hypothesis from easy to hard via self-learning discriminative representations from coarse to fine scales. The face detection problem is considered as solving three sub-problems: eliminating easy background with a simple but fast model, then localising the face region with a soft-cascade, followed by precise detection and localisation by verifying retained regions with a deeper and stronger model. The face detector is trained on the AFLW dataset following the standard evaluation procedure, and the method is tested on four other public datasets, i.e. FDDB, AFW, CMU-MIT and GENKI. Both quantitative and qualitative results on FDDB and AFW are reported, which show promising performances on detecting faces in unconstrained environment.