16,983 research outputs found
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications
Facial expressions are an important way through which humans interact
socially. Building a system capable of automatically recognizing facial
expressions from images and video has been an intense field of study in recent
years. Interpreting such expressions remains challenging and much research is
needed about the way they relate to human affect. This paper presents a general
overview of automatic RGB, 3D, thermal and multimodal facial expression
analysis. We define a new taxonomy for the field, encompassing all steps from
face detection to facial expression recognition, and describe and classify the
state of the art methods accordingly. We also present the important datasets
and the bench-marking of most influential methods. We conclude with a general
discussion about trends, important questions and future lines of research
A Survey on Periocular Biometrics Research
Periocular refers to the facial region in the vicinity of the eye, including
eyelids, lashes and eyebrows. While face and irises have been extensively
studied, the periocular region has emerged as a promising trait for
unconstrained biometrics, following demands for increased robustness of face or
iris systems. With a surprisingly high discrimination ability, this region can
be easily obtained with existing setups for face and iris, and the requirement
of user cooperation can be relaxed, thus facilitating the interaction with
biometric systems. It is also available over a wide range of distances even
when the iris texture cannot be reliably obtained (low resolution) or under
partial face occlusion (close distances). Here, we review the state of the art
in periocular biometrics research. A number of aspects are described,
including: i) existing databases, ii) algorithms for periocular detection
and/or segmentation, iii) features employed for recognition, iv) identification
of the most discriminative regions of the periocular area, v) comparison with
iris and face modalities, vi) soft-biometrics (gender/ethnicity
classification), and vii) impact of gender transformation and plastic surgery
on the recognition accuracy. This work is expected to provide an insight of the
most relevant issues in periocular biometrics, giving a comprehensive coverage
of the existing literature and current state of the art.Comment: Published in Pattern Recognition Letter
Vision-based Human Gender Recognition: A Survey
Gender is an important demographic attribute of people. This paper provides a
survey of human gender recognition in computer vision. A review of approaches
exploiting information from face and whole body (either from a still image or
gait sequence) is presented. We highlight the challenges faced and survey the
representative methods of these approaches. Based on the results, good
performance have been achieved for datasets captured under controlled
environments, but there is still much work that can be done to improve the
robustness of gender recognition under real-life environments.Comment: 30 page
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
We present an algorithm for simultaneous face detection, landmarks
localization, pose estimation and gender recognition using deep convolutional
neural networks (CNN). The proposed method called, HyperFace, fuses the
intermediate layers of a deep CNN using a separate CNN followed by a multi-task
learning algorithm that operates on the fused features. It exploits the synergy
among the tasks which boosts up their individual performances. Additionally, we
propose two variants of HyperFace: (1) HyperFace-ResNet that builds on the
ResNet-101 model and achieves significant improvement in performance, and (2)
Fast-HyperFace that uses a high recall fast face detector for generating region
proposals to improve the speed of the algorithm. Extensive experiments show
that the proposed models are able to capture both global and local information
in faces and performs significantly better than many competitive algorithms for
each of these four tasks.Comment: Accepted in Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
Hierarchical Representation Learning for Kinship Verification
Kinship verification has a number of applications such as organizing large
collections of images and recognizing resemblances among humans. In this
research, first, a human study is conducted to understand the capabilities of
human mind and to identify the discriminatory areas of a face that facilitate
kinship-cues. Utilizing the information obtained from the human study, a
hierarchical Kinship Verification via Representation Learning (KVRL) framework
is utilized to learn the representation of different face regions in an
unsupervised manner. We propose a novel approach for feature representation
termed as filtered contractive deep belief networks (fcDBN). The proposed
feature representation encodes relational information present in images using
filters and contractive regularization penalty. A compact representation of
facial images of kin is extracted as an output from the learned model and a
multi-layer neural network is utilized to verify the kin accurately. A new WVU
Kinship Database is created which consists of multiple images per subject to
facilitate kinship verification. The results show that the proposed deep
learning framework (KVRL-fcDBN) yields stateof-the-art kinship verification
accuracy on the WVU Kinship database and on four existing benchmark datasets.
Further, kinship information is used as a soft biometric modality to boost the
performance of face verification via product of likelihood ratio and support
vector machine based approaches. Using the proposed KVRL-fcDBN framework, an
improvement of over 20% is observed in the performance of face verification
Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks
Generally, facial age variations affect gender classification accuracy
significantly, because facial shape and skin texture change as they grow old.
This requires re-examination on the gender classification system to consider
facial age information. In this paper, we propose Multi-expert Gender
Classification on Age Group (MGA), an end-to-end multi-task learning schemes of
age estimation and gender classification. First, two types of deep neural
networks are utilized; Convolutional Appearance Network (CAN) for facial
appearance feature and Deep Geometry Network (DGN) for facial geometric
feature. Then, CAN and DGN are integrated by the proposed model integration
strategy and fine-tuned in order to improve age and gender classification
accuracy. The facial images are categorized into one of three age groups
(young, adult and elder group) based on their estimated age, and the system
makes a gender prediction according to average fusion strategy of three gender
classification experts, which are trained to fit gender characteristics of each
age group. Rigorous experimental results conducted on the challenging databases
suggest that the proposed MGA outperforms several state-of-art researches with
smaller computational cost.Comment: 12 page
Relevant features for Gender Classification in NIR Periocular Images
Most gender classifications methods from NIR images have used iris
information. Recent work has explored the use of the whole periocular iris
region which has surprisingly achieve better results. This suggests the most
relevant information for gender classification is not located in the iris as
expected. In this work, we analyze and demonstrate the location of the most
relevant features that describe gender in periocular NIR images and evaluate
its influence its classification. Experiments show that the periocular region
contains more gender information than the iris region. We extracted several
features (intensity, texture, and shape) and classified them according to its
relevance using the XgBoost algorithm. Support Vector Machine and nine ensemble
classifiers were used for testing gender accuracy when using the most relevant
features. The best classification results were obtained when 4,000 features
located on the periocular region were used (89.22\%). Additional experiments
with the full periocular iris images versus the iris-Occluded images were
performed. The gender classification rates obtained were 84.35\% and 85.75\%
respectively. We also contribute to the state of the art with a new database
(UNAB-Gender). From results, we suggest focussing only on the surrounding area
of the iris. This allows us to realize a faster classification of gender from
NIR periocular images.Comment: 12 pages, Paper accepted by IET Biometric
Segment-based Methods for Facial Attribute Detection from Partial Faces
State-of-the-art methods of attribute detection from faces almost always
assume the presence of a full, unoccluded face. Hence, their performance
degrades for partially visible and occluded faces. In this paper, we introduce
SPLITFACE, a deep convolutional neural network-based method that is explicitly
designed to perform attribute detection in partially occluded faces. Taking
several facial segments and the full face as input, the proposed method takes a
data driven approach to determine which attributes are localized in which
facial segments. The unique architecture of the network allows each attribute
to be predicted by multiple segments, which permits the implementation of
committee machine techniques for combining local and global decisions to boost
performance. With access to segment-based predictions, SPLITFACE can predict
well those attributes which are localized in the visible parts of the face,
without having to rely on the presence of the whole face. We use the CelebA and
LFWA facial attribute datasets for standard evaluations. We also modify both
datasets, to occlude the faces, so that we can evaluate the performance of
attribute detection algorithms on partial faces. Our evaluation shows that
SPLITFACE significantly outperforms other recent methods especially for partial
faces
Face Recognition: A Novel Multi-Level Taxonomy based Survey
In a world where security issues have been gaining growing importance, face
recognition systems have attracted increasing attention in multiple application
areas, ranging from forensics and surveillance to commerce and entertainment.
To help understanding the landscape and abstraction levels relevant for face
recognition systems, face recognition taxonomies allow a deeper dissection and
comparison of the existing solutions. This paper proposes a new, more
encompassing and richer multi-level face recognition taxonomy, facilitating the
organization and categorization of available and emerging face recognition
solutions; this taxonomy may also guide researchers in the development of more
efficient face recognition solutions. The proposed multi-level taxonomy
considers levels related to the face structure, feature support and feature
extraction approach. Following the proposed taxonomy, a comprehensive survey of
representative face recognition solutions is presented. The paper concludes
with a discussion on current algorithmic and application related challenges
which may define future research directions for face recognition.Comment: This paper is a preprint of a paper submitted to IET Biometrics. If
accepted, the copy of record will be available at the IET Digital Librar
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
Photorealistic frontal view synthesis from a single face image has a wide
range of applications in the field of face recognition. Although data-driven
deep learning methods have been proposed to address this problem by seeking
solutions from ample face data, this problem is still challenging because it is
intrinsically ill-posed. This paper proposes a Two-Pathway Generative
Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by
simultaneously perceiving global structures and local details. Four landmark
located patch networks are proposed to attend to local textures in addition to
the commonly used global encoder-decoder network. Except for the novel
architecture, we make this ill-posed problem well constrained by introducing a
combination of adversarial loss, symmetry loss and identity preserving loss.
The combined loss function leverages both frontal face distribution and
pre-trained discriminative deep face models to guide an identity preserving
inference of frontal views from profiles. Different from previous deep learning
methods that mainly rely on intermediate features for recognition, our method
directly leverages the synthesized identity preserving image for downstream
tasks like face recognition and attribution estimation. Experimental results
demonstrate that our method not only presents compelling perceptual results but
also outperforms state-of-the-art results on large pose face recognition.Comment: accepted at ICCV 2017, main paper & supplementary material, 11 page
- …