3 research outputs found
Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network
The accurate localization of facial landmarks is at the core of face analysis
tasks, such as face recognition and facial expression analysis, to name a few.
In this work we propose a novel localization approach based on a Deep Learning
architecture that utilizes dual cascaded CNN subnetworks of the same length,
where each subnetwork in a cascade refines the accuracy of its predecessor. The
first set of cascaded subnetworks estimates heatmaps that encode the landmarks'
locations, while the second set of cascaded subnetworks refines the
heatmaps-based localization using regression, and also receives as input the
output of the corresponding heatmap estimation subnetwork. The proposed scheme
is experimentally shown to compare favorably with contemporary state-of-the-art
schemes
CTrGAN: Cycle Transformers GAN for Gait Transfer
We attempt for the first time to address the problem of gait transfer. In
contrast to motion transfer, the objective here is not to imitate the source's
normal motions, but rather to transform the source's motion into a typical gait
pattern for the target. Using gait recognition models, we demonstrate that
existing techniques yield a discrepancy that can be easily detected. We
introduce a novel model, Cycle Transformers GAN (CTrGAN), that can successfully
generate the target's natural gait. CTrGAN's generators consist of a decoder
and encoder, both Transformers, where the attention is on the temporal domain
between complete images rather than the spatial domain between patches. While
recent Transformer studies in computer vision mainly focused on discriminative
tasks, we introduce an architecture that can be applied to synthesis tasks.
Using a widely-used gait recognition dataset, we demonstrate that our approach
is capable of producing over an order of magnitude more realistic personalized
gaits than existing methods, even when used with sources that were not
available during training
The FG 2015 Kinship Verification in the Wild Evaluation
International audienceThe aim of the Kinship Verification in the Wild Evaluation (held in conjunction with the 2015 IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, Slovenia) was to evaluate different kinship verification algorithms. For this task, two datasets were made available and three possible experimental protocols (unsupervised, image-restricted, and image-unrestricted) were designed. Five institutions submitted their results to the evaluation: (i) Politecnico di Torino, Italy; (ii) LIRIS-University of Lyon, France; (iii) Universidad de Las Palmas de Gran Canaria, Spain; (iv) Nanjing University of Aeronautics and Astronautics, China; and (v) Bar Ilan University, Israel. Most of the participants tackled the image-restricted challenge and experimental results demonstrated better kinship verification performance than the baseline methods provided by the organizers