The Fastest Deformable Part Model for Object Detection

This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in de-tection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D cor-relation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D cor-relation can be calculated by more efficient linear combi-nation of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a dis-criminative manner. For cascade part pruning, neighbor-hood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order ap-proximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orien-tation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the pro-posed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the pro-posed method achieves state-of-the-art accuracy on pedes-trian and face detection task with frame-rate speed. 1

Occlusion Coherence: Detecting and Localizing Occluded Faces

The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and landmark localization that explicitly models part occlusion. The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances. This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model. We test the model on several benchmarks for landmark localization and detection including challenging new data sets featuring significant occlusion. We find that the addition of an explicit occlusion model yields a detection system that outperforms existing approaches for occluded instances while maintaining competitive accuracy in detection and landmark localization for unoccluded instances

A Deep Pyramid Deformable Part Model for Face Detection

We present a face detection algorithm based on Deformable Part Models and deep pyramidal features. The proposed method called DP2MFD is able to detect faces of various sizes and poses in unconstrained conditions. It reduces the gap in training and testing of DPM on deep features by adding a normalization layer to the deep convolutional neural network (CNN). Extensive experiments on four publicly available unconstrained face detection datasets show that our method is able to capture the meaningful structure of faces and performs significantly better than many competitive face detection algorithms