Gary B. Huang

TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.

TL;DR: Improved methods are summarized and the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster is presented, reducing the effort needed to answer circuit questions and providing procedures linking the neurons defined by the analysis with genetic reagents.

TL;DR: A review of the contributions to LFW for which the authors have provided results to the curators and the cross cutting topic of alignment and how it is used in various methods is reviewed.

TL;DR: It is shown that a recognition system using only representations obtained from deep learning can achieve comparable accuracy with a system using a combination of hand-crafted image descriptors, and empirically show that learning weights not only is necessary for obtaining good multilayer representations, but also provides robustness to the choice of the network architecture parameters.

TL;DR: The alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm specifically developed for and trained on hand-labeled face images.