C
Carlos D. Castillo
Researcher at University of Maryland, College Park
Publications - 106
Citations - 5510
Carlos D. Castillo is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Facial recognition system & Convolutional neural network. The author has an hindex of 31, co-authored 98 publications receiving 4111 citations. Previous affiliations of Carlos D. Castillo include University of Chile & Simón Bolívar University.
Papers
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Proceedings ArticleDOI
Frontal to profile face verification in the wild
Soumyadip Sengupta,Jun-Cheng Chen,Carlos D. Castillo,Vishal M. Patel,Rama Chellappa,David W. Jacobs +5 more
TL;DR: The aim of this data set is to isolate the factor of pose variation in terms of extreme poses like profile, where many features are occluded, along with other `in the wild' variations to suggest that there is a gap between human performance and automatic face recognition methods for large pose variations in unconstrained images.
Proceedings ArticleDOI
Generate to Adapt: Aligning Domains Using Generative Adversarial Networks
TL;DR: This work proposes an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space by inducing a symbiotic relationship between the learned embedding and a generative adversarial network.
Patent
L2 constrained softmax loss for discriminative face verification
TL;DR: This paper adds an L2-constraint to the feature descriptors which restricts them to lie on a hypersphere of a fixed radius and shows that integrating this simple step in the training pipeline significantly boosts the performance of face verification.
Proceedings ArticleDOI
An All-In-One Convolutional Neural Network for Face Analysis
TL;DR: A multi-purpose algorithm for simultaneous face detection, face alignment, pose estimation, gender recognition, smile detection, age estimation and face recognition using a single deep convolutional neural network.
Proceedings ArticleDOI
SfSNet: Learning Shape, Reflectance and Illuminance of Faces 'in the Wild'
TL;DR: SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation and is designed to reflect a physical lambertian rendering model.