Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition

Two approaches are proposed for cross-pose face recognition, one is based on the 3D reconstruction of facial components and the other is based on the deep Convolutional Neural Network (CNN). Unlike most 3D approaches that consider holistic faces, the proposed approach considers 3D facial components. It segments a 2D gallery face into components, reconstructs the 3D surface for each component, and recognizes a probe face by component features. The segmentation is based on the landmarks located by a hierarchical algorithm that combines the Faster R-CNN for face detection and the Reduced Tree Structured Model for landmark localization. The core part of the CNN-based approach is a revised VGG network. We study the performances with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. We investigate the performances of the network when it is employed as a classifier or designed as a feature extractor. The two recognition approaches and the fast landmark localization are evaluated in extensive experiments, and compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table

Flash-lag chimeras: the role of perceived alignment in the composite face effect

Spatial alignment of different face halves results in a configuration that mars the recognition of the identity of either face half (). What would happen to the recognition performance for face halves that were aligned on the retina but were perceived as misaligned, or were misaligned on the retina but were perceived as aligned? We used the 'flash-lag' effect () to address these questions. We created chimeras consisting of a stationary top half-face initially aligned with a moving bottom half-face. Flash-lag chimeras were better recognized than their stationary counterparts. However when flashed face halves were presented physically ahead of moving halves thereby nulling the flash-lag effect, recognition was impaired. This counters the notion that relative movement between the two face halves per se is sufficient to explain better recognition of flash-lag chimeras. Thus, the perceived spatial alignment of face halves (despite retinal misalignment) impairs recognition, while perceived misalignment (despite retinal alignment) does not

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