Deep convolutional neural networks (CNNs) based approaches are the state-of-the-art in various computer vision tasks, including face recognition. Considerable research effort is currently being directed towards further improving deep CNNs by focusing on more powerful model architectures and better learning techniques. However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce in the literature. In this paper, we try to fill this gap and study the effects of different covariates on the verification performance of four recent deep CNN models using the Labeled Faces in the Wild (LFW) dataset. Specifically, we investigate the influence of covariates related to: image quality -blur, JPEG compression, occlusion, noise, image brightness, contrast, missing pixels; and model characteristics -CNN architecture, color information, descriptor computation; and analyze their impact on the face verification performance of AlexNet, VGG-Face, GoogLeNet, and SqueezeNet. Based on comprehensive and rigorous experimentation, we identify the strengths and weaknesses of the deep learning models, and present key areas for potential future research. Our results indicate that high levels of noise, blur, missing pixels, and brightness have a detrimental effect on the verification performance of all models, whereas the impact of contrast changes and compression artifacts is limited. It has been found that the descriptor computation strategy and color information does not have a significant influence on performance.

We created a new object-oriented environment for MR pulse-sequence development based on IDEA VE11C and above versions using an Open Science philosophy. This Ginkgo toolkit uses a modular structure to facilitate the design of pulse sequences using the aggregation of basic open-source sequence blocks available from the toolkit. Proofs of concept of the productivity gain reached using Ginkgo are provided through the implementation of a series of sequence models including a diffusion-weighted PGSE 3D EPI sequence.