16,821 research outputs found
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
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
Face analytics benefits many multimedia applications. It consists of a number
of tasks, such as facial emotion recognition and face parsing, and most
existing approaches generally treat these tasks independently, which limits
their deployment in real scenarios. In this paper we propose an integrated Face
Analytics Network (iFAN), which is able to perform multiple tasks jointly for
face analytics with a novel carefully designed network architecture to fully
facilitate the informative interaction among different tasks. The proposed
integrated network explicitly models the interactions between tasks so that the
correlations between tasks can be fully exploited for performance boost. In
addition, to solve the bottleneck of the absence of datasets with comprehensive
training data for various tasks, we propose a novel cross-dataset hybrid
training strategy. It allows "plug-in and play" of multiple datasets annotated
for different tasks without the requirement of a fully labeled common dataset
for all the tasks. We experimentally show that the proposed iFAN achieves
state-of-the-art performance on multiple face analytics tasks using a single
integrated model. Specifically, iFAN achieves an overall F-score of 91.15% on
the Helen dataset for face parsing, a normalized mean error of 5.81% on the
MTFL dataset for facial landmark localization and an accuracy of 45.73% on the
BNU dataset for emotion recognition with a single model.Comment: 10 page
LEARNet Dynamic Imaging Network for Micro Expression Recognition
Unlike prevalent facial expressions, micro expressions have subtle,
involuntary muscle movements which are short-lived in nature. These minute
muscle movements reflect true emotions of a person. Due to the short duration
and low intensity, these micro-expressions are very difficult to perceive and
interpret correctly. In this paper, we propose the dynamic representation of
micro-expressions to preserve facial movement information of a video in a
single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to
capture micro-level features of an expression in the facial region. The LEARNet
refines the salient expression features in accretive manner by incorporating
accretion layers (AL) in the network. The response of the AL holds the hybrid
feature maps generated by prior laterally connected convolution layers.
Moreover, LEARNet architecture incorporates the cross decoupled relationship
between convolution layers which helps in preserving the tiny but influential
facial muscle change information. The visual responses of the proposed LEARNet
depict the effectiveness of the system by preserving both high- and micro-level
edge features of facial expression. The effectiveness of the proposed LEARNet
is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC.
The experimental results after investigation show a significant improvement of
4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II,
CAS(ME)^2 and SMIC datasets respectively.Comment: Dynamic imaging, accretion, lateral, micro expression recognitio
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