2,070 research outputs found
Facial soft biometrics for recognition in the wild: recent works, annotation and COTS evaluation
The role of soft biometrics to enhance person recognition
systems in unconstrained scenarios has not been extensively
studied. Here, we explore the utility of the following modalities:
gender, ethnicity, age, glasses, beard and moustache. We consider
two assumptions: i) manual estimation of soft biometrics, and
ii) automatic estimation from two Commercial Off-The-Shelf
systems (COTS). All experiments are reported using the LFW
database. First, we study the discrimination capabilities of soft
biometrics standalone. Then, experiments are carried out fusing
soft biometrics with two state-of-the-art face recognition systems
based on deep learning. We observe that soft biometrics is
a valuable complement to the face modality in unconstrained
scenarios, with relative improvements up to 40%=15% in the
verification performance when using manual/automatic soft biometrics
estimation. Results are reproducible as we make public
our manual annotations and COTS outputs of soft biometrics
over LFW, as well as the face recognition scoresThis work was funded by Spanish Guardia Civil and project CogniMetrics (TEC2015-70627-R) from MINECO/FEDE
From clothing to identity; manual and automatic soft biometrics
Soft biometrics have increasingly attracted research interest and are often considered as major cues for identity, especially in the absence of valid traditional biometrics, as in surveillance. In everyday life, several incidents and forensic scenarios highlight the usefulness and capability of identity information that can be deduced from clothing. Semantic clothing attributes have recently been introduced as a new form of soft biometrics. Although clothing traits can be naturally described and compared by humans for operable and successful use, it is desirable to exploit computer-vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This study proposes a novel set of soft clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way we can explore the capability of human attributes vis-a-vis those which are inferred automatically by computer-vision. Categorical and comparative soft clothing traits are derived and used for identification/re identification either to supplement soft body traits or to be used alone. The automatically- and manually-derived soft clothing biometrics are employed in challenging invariant person retrieval. The experimental results highlight promising potential for use in various applications
Soft Biometric Analysis: MultiPerson and RealTime Pedestrian Attribute Recognition in Crowded Urban Environments
Traditionally, recognition systems were only based on human hard biometrics. However,
the ubiquitous CCTV cameras have raised the desire to analyze human biometrics from
far distances, without people attendance in the acquisition process. Highresolution
face closeshots
are rarely available at far distances such that facebased
systems cannot
provide reliable results in surveillance applications. Human soft biometrics such as body
and clothing attributes are believed to be more effective in analyzing human data collected
by security cameras.
This thesis contributes to the human soft biometric analysis in uncontrolled environments
and mainly focuses on two tasks: Pedestrian Attribute Recognition (PAR) and person reidentification
(reid).
We first review the literature of both tasks and highlight the history
of advancements, recent developments, and the existing benchmarks. PAR and person reid
difficulties are due to significant distances between intraclass
samples, which originate
from variations in several factors such as body pose, illumination, background, occlusion,
and data resolution. Recent stateoftheart
approaches present endtoend
models that
can extract discriminative and comprehensive feature representations from people. The
correlation between different regions of the body and dealing with limited learning data
is also the objective of many recent works. Moreover, class imbalance and correlation
between human attributes are specific challenges associated with the PAR problem.
We collect a large surveillance dataset to train a novel gender recognition model suitable
for uncontrolled environments. We propose a deep residual network that extracts several
posewise
patches from samples and obtains a comprehensive feature representation. In
the next step, we develop a model for multiple attribute recognition at once. Considering
the correlation between human semantic attributes and class imbalance, we respectively
use a multitask
model and a weighted loss function. We also propose a multiplication
layer on top of the backbone features extraction layers to exclude the background features
from the final representation of samples and draw the attention of the model to the
foreground area.
We address the problem of person reid
by implicitly defining the receptive fields of
deep learning classification frameworks. The receptive fields of deep learning models
determine the most significant regions of the input data for providing correct decisions.
Therefore, we synthesize a set of learning data in which the destructive regions (e.g.,
background) in each pair of instances are interchanged. A segmentation module
determines destructive and useful regions in each sample, and the label of synthesized
instances are inherited from the sample that shared the useful regions in the synthesized
image. The synthesized learning data are then used in the learning phase and help
the model rapidly learn that the identity and background regions are not correlated.
Meanwhile, the proposed solution could be seen as a data augmentation approach that
fully preserves the label information and is compatible with other data augmentation
techniques.
When reid
methods are learned in scenarios where the target person appears with identical garments in the gallery, the visual appearance of clothes is given the most
importance in the final feature representation. Clothbased
representations are not
reliable in the longterm
reid
settings as people may change their clothes. Therefore,
developing solutions that ignore clothing cues and focus on identityrelevant
features are
in demand. We transform the original data such that the identityrelevant
information of
people (e.g., face and body shape) are removed, while the identityunrelated
cues (i.e.,
color and texture of clothes) remain unchanged. A learned model on the synthesized
dataset predicts the identityunrelated
cues (shortterm
features). Therefore, we train a
second model coupled with the first model and learns the embeddings of the original data
such that the similarity between the embeddings of the original and synthesized data is
minimized. This way, the second model predicts based on the identityrelated
(longterm)
representation of people.
To evaluate the performance of the proposed models, we use PAR and person reid
datasets, namely BIODI, PETA, RAP, Market1501,
MSMTV2,
PRCC, LTCC, and MIT
and compared our experimental results with stateoftheart
methods in the field.
In conclusion, the data collected from surveillance cameras have low resolution, such
that the extraction of hard biometric features is not possible, and facebased
approaches
produce poor results. In contrast, soft biometrics are robust to variations in data quality.
So, we propose approaches both for PAR and person reid
to learn discriminative features
from each instance and evaluate our proposed solutions on several publicly available
benchmarks.This thesis was prepared at the University of Beria Interior, IT Instituto de Telecomunicações, Soft Computing and Image Analysis Laboratory (SOCIA Lab), Covilhã Delegation, and was submitted to the University of Beira Interior for defense in a public examination session
Human Gait Recognition Subject to Different Covariate Factors in a Multi-View Environment
Human gait recognition system identifies individuals based on their biometric traits. A human’s biometric features can be grouped into physiologic or behavioral traits. Biometric traits, such as the face [1], ears [2], iris [3], finger prints, passwords, and tokens, require highly accurate recognition and a well-controlled human interaction to be effective. In contrast, behavioral traits such as voice, signature, and gait do not require any human interaction and can be collected in a hidden and non-invasive mode with a camera system at a low resolution. In comparison with other physiological traits, one of the main advantages of gait analysis is the collection of data from a certain distance. However, gait is less powerful than physiological traits, yet it still has widespread application in surveillance for unfavorable situations. From traditional algorithms to deep learning models, a gait survey provides a detailed history of gait recognition
Identification of persons on the basis of soft biometric and non-biometric traits
Cílem bakalářské práce je studie možností identifikace osob na základě sekundárních biometrických a ne-biometrických znaků, výběr vhodných sekundárních biometrických a ne-biometrických znaků, následné ověření úspěšnosti metody identifikace nebo částečné identifikace osob na reálných datech a implementace metody pomocí jazyků HTML, CSS, PHP a MySQL.The aim of bachelor thesis is study the possibility of identifying individuals based on soft biometric and non-biometric traits, the selection of appropriate soft biometric and non-biometric traits, subsequent verification success identification methods or partial identification of persons on real data and implementation methods with HTML, CSS, PHP and MySQL.
Multiple classifiers in biometrics. part 1: Fundamentals and review
We provide an introduction to Multiple Classifier Systems (MCS) including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods. This introduction complements other existing overviews of MCS, as here we also review the most prevalent theoretical framework for MCS and discuss theoretical developments related to MCS
The introduction to MCS is then followed by a review of the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. This review includes general descriptions of successful MCS methods and architectures in order to facilitate the export of them to other information fusion problems.
Based on the theory and framework introduced here, in the companion paper we then develop in more technical detail recent trends and developments in MCS from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in the present paper, methods in the companion paper are introduced in a general way so they can be applied to other information fusion problems as well. Finally, also in the companion paper, we discuss open challenges in biometrics and the role of MCS to advance themThis work was funded by projects CogniMetrics (TEC2015-70627-R)
from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of thisthis work was conducted during a research visit of J.F. to Prof. Ludmila Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE
HUMAN GENDER CLASSIFICATION USING KINECT SENSOR: A REVIEW
Human Gender Classification using Kinect sensor aims to classifying people’s gender based on their outward appearance. Application areas of Kinect sensor technology includes security, marketing, healthcare, and gaming. However, because of the changes in pose, attire, and illumination, gender determination with the Kinect sensor is not a trivial task. It is based on a variety of characteristics, including biological, social network, face, and body aspects. In recent years, gender classification that utilizes the Kinect sensor became a popular and essential way for accurate gender classification. A variety of methods and approaches, like machine learning, convolutional neural networks, sport vector machine (SVM), etc., have been used for gender classification using a Kinect sensor. This paper presents the state of the art for gender classification, with a focus on the features, databases, procedures, and algorithms used in it. A review of recent studies on this subject using the Kinect sensor and other technologies is provided, together with information on the variables that affect the classification\u27s accuracy. In addition, several publicly accessible databases or datasets are used by researchers to classify people by gender are covered. Finlay, this overview offers insightful information about the potential future avenues for research on Kinect-based human gender classification
Person recognition based on deep gait: a survey.
Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future
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