Empirical analysis of cascade deformable models for multi-view face detection

In this paper, we present a face detector based on Cascade Deformable Part Models (CDPM) [1]. Our model is learnt from partially labelled images using Latent Support Vector Machines (LSVM). Recently Zhu et al. [2] proposed a Tree StructureModel for multi-view face detection trained with facial landmark labels, which resulted on a complex and suboptimal system for face detection. Instead, we adopt CDPMs enhanced with a data-mining procedure to enrich models during the LSVM training. Furthermore, a post-optimization procedure is derived to improve the performance of the CDPMs. Experimental results show that the proposed model can deal with highly expressive and partially occluded faces while outperforming the state-of-the-art face detectors by a large margin on challenging benchmarks such as the FDDB [3] and the AFLW [4] databases.