1,171 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
Multi-view Face Detection Using Deep Convolutional Neural Networks
In this paper we consider the problem of multi-view face detection. While
there has been significant research on this problem, current state-of-the-art
approaches for this task require annotation of facial landmarks, e.g. TSM [25],
or annotation of face poses [28, 22]. They also require training dozens of
models to fully capture faces in all orientations, e.g. 22 models in HeadHunter
method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method
that does not require pose/landmark annotation and is able to detect faces in a
wide range of orientations using a single model based on deep convolutional
neural networks. The proposed method has minimal complexity; unlike other
recent deep learning object detection methods [9], it does not require
additional components such as segmentation, bounding-box regression, or SVM
classifiers. Furthermore, we analyzed scores of the proposed face detector for
faces in different orientations and found that 1) the proposed method is able
to detect faces from different angles and can handle occlusion to some extent,
2) there seems to be a correlation between dis- tribution of positive examples
in the training set and scores of the proposed face detector. The latter
suggests that the proposed methods performance can be further improved by using
better sampling strategies and more sophisticated data augmentation techniques.
Evaluations on popular face detection benchmark datasets show that our
single-model face detector algorithm has similar or better performance compared
to the previous methods, which are more complex and require annotations of
either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR
MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
Face tracking serves as the crucial initial step in mobile applications
trying to analyse target faces over time in mobile settings. However, this
problem has received little attention, mainly due to the scarcity of dedicated
face tracking benchmarks. In this work, we introduce MobiFace, the first
dataset for single face tracking in mobile situations. It consists of 80
unedited live-streaming mobile videos captured by 70 different smartphone users
in fully unconstrained environments. Over bounding boxes are manually
labelled. The videos are carefully selected to cover typical smartphone usage.
The videos are also annotated with 14 attributes, including 6 newly proposed
attributes and 8 commonly seen in object tracking. 36 state-of-the-art
trackers, including facial landmark trackers, generic object trackers and
trackers that we have fine-tuned or improved, are evaluated. The results
suggest that mobile face tracking cannot be solved through existing approaches.
In addition, we show that fine-tuning on the MobiFace training data
significantly boosts the performance of deep learning-based trackers,
suggesting that MobiFace captures the unique characteristics of mobile face
tracking. Our goal is to offer the community a diverse dataset to enable the
design and evaluation of mobile face trackers. The dataset, annotations and the
evaluation server will be on \url{https://mobiface.github.io/}.Comment: To appear on The 14th IEEE International Conference on Automatic Face
and Gesture Recognition (FG 2019
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