290 research outputs found
Covariate conscious approach for Gait recognition based upon Zernike moment invariants
Gait recognition i.e. identification of an individual from his/her walking
pattern is an emerging field. While existing gait recognition techniques
perform satisfactorily in normal walking conditions, there performance tend to
suffer drastically with variations in clothing and carrying conditions. In this
work, we propose a novel covariate cognizant framework to deal with the
presence of such covariates. We describe gait motion by forming a single 2D
spatio-temporal template from video sequence, called Average Energy Silhouette
image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the
parts of AESI infected with covariates. Following this, features are extracted
from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of
Directional Pixels (MDPs) methods. The obtained features are fused together to
form the final well-endowed feature set. Experimental evaluation of the
proposed framework on three publicly available datasets i.e. CASIA dataset B,
OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently
published gait recognition approaches, prove its superior performance.Comment: 11 page
Performance comparison of intrusion detection systems and application of machine learning to Snort system
This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort
A Survey on Ear Biometrics
Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers
A Survey on Biometrics and Cancelable Biometrics Systems
Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results
Uniscale and multiscale gait recognition in realistic scenario
The performance of a gait recognition method is affected by numerous challenging
factors that degrade its reliability as a behavioural biometrics for subject identification in
realistic scenario. Thus for effective visual surveillance, this thesis presents five gait recog-
nition methods that address various challenging factors to reliably identify a subject in
realistic scenario with low computational complexity. It presents a gait recognition method
that analyses spatio-temporal motion of a subject with statistical and physical parameters
using Procrustes shape analysis and elliptic Fourier descriptors (EFD). It introduces a part-
based EFD analysis to achieve invariance to carrying conditions, and the use of physical
parameters enables it to achieve invariance to across-day gait variation. Although spatio-
temporal deformation of a subjectās shape in gait sequences provides better discriminative
power than its kinematics, inclusion of dynamical motion characteristics improves the iden-
tification rate. Therefore, the thesis presents a gait recognition method which combines
spatio-temporal shape and dynamic motion characteristics of a subject to achieve robust-
ness against the maximum number of challenging factors compared to related state-of-the-
art methods. A region-based gait recognition method that analyses a subjectās shape in
image and feature spaces is presented to achieve invariance to clothing variation and carry-
ing conditions. To take into account of arbitrary moving directions of a subject in realistic
scenario, a gait recognition method must be robust against variation in view. Hence, the the-
sis presents a robust view-invariant multiscale gait recognition method. Finally, the thesis
proposes a gait recognition method based on low spatial and low temporal resolution video
sequences captured by a CCTV. The computational complexity of each method is analysed.
Experimental analyses on public datasets demonstrate the efficacy of the proposed methods
Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis
This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multiview gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness
Advancing iris biometric technology
PhD ThesisThe iris biometric is a well-established technology which is already in use in
several nation-scale applications and it is still an active research area with several
unsolved problems. This work focuses on three key problems in iris biometrics
namely: segmentation, protection and cross-matching. Three novel
methods in each of these areas are proposed and analyzed thoroughly.
In terms of iris segmentation, a novel iris segmentation method is designed
based on a fusion of an expanding and a shrinking active contour by integrating
a new pressure force within the Gradient Vector Flow (GVF) active
contour model. In addition, a new method for closed eye detection is proposed.
The experimental results on the CASIA V4, MMU2, UBIRIS V1 and
UBIRIS V2 databases show that the proposed method achieves state-of-theart
results in terms of segmentation accuracy and recognition performance
while being computationally more efficient. In this context, improvements
by 60.5%, 42% and 48.7% are achieved in segmentation accuracy for the
CASIA V4, MMU2 and UBIRIS V1 databases, respectively. For the UBIRIS
V2 database, a superior time reduction is reported (85.7%) while maintaining
a similar accuracy. Similarly, considerable time improvements by 63.8%,
56.6% and 29.3% are achieved for the CASIA V4, MMU2 and UBIRIS V1
databases, respectively.
With respect to iris biometric protection, a novel security architecture is designed
to protect the integrity of iris images and templates using watermarking
and Visual Cryptography (VC). Firstly, for protecting the iris image, text
which carries personal information is embedded in the middle band frequency
region of the iris image using a novel watermarking algorithm that randomly
interchanges multiple middle band pairs of the Discrete Cosine Transform
(DCT). Secondly, for iris template protection, VC is utilized to protect the
iii
iris template. In addition, the integrity of the stored template in the biometric
smart card is guaranteed by using the hash signatures. The proposed method
has a minimal effect on the iris recognition performance of only 3.6% and
4.9% for the CASIA V4 and UBIRIS V1 databases, respectively. In addition,
the VC scheme is designed to be readily applied to protect any biometric binary
template without any degradation to the recognition performance with a
complexity of only O(N).
As for cross-spectral matching, a framework is designed which is capable of
matching iris images in different lighting conditions. The first method is designed
to work with registered iris images where the key idea is to synthesize
the corresponding Near Infra-Red (NIR) images from the Visible Light (VL)
images using an Artificial Neural Network (ANN) while the second method
is capable of working with unregistered iris images based on integrating the
Gabor filter with different photometric normalization models and descriptors
along with decision level fusion to achieve the cross-spectral matching. A
significant improvement by 79.3% in cross-spectral matching performance is
attained for the UTIRIS database. As for the PolyU database, the proposed
verification method achieved an improvement by 83.9% in terms of NIR vs
Red channel matching which confirms the efficiency of the proposed method.
In summary, the most important open issues in exploiting the iris biometric
are presented and novel methods to address these problems are proposed.
Hence, this work will help to establish a more robust iris recognition system
due to the development of an accurate segmentation method working for iris
images taken under both the VL and NIR. In addition, the proposed protection
scheme paves the way for a secure iris images and templates storage.
Moreover, the proposed framework for cross-spectral matching will help to
employ the iris biometric in several security applications such as surveillance
at-a-distance and automated watch-list identification.Ministry of Higher Education and
Scientific Research in Ira
Biometric information analyses using computer vision techniques.
Biometric information analysis is derived from the analysis of a series of physical and biological characteristics of a person. It is widely regarded as the most fundamental task in the realms of computer vision and machine learning. With the overwhelming power of computer vision techniques, biometric information analysis have received increasing attention in the past decades. Biometric information can be analyzed from many sources including iris, retina, voice, ļ¬ngerprint, facial image or even the way one walks with. Facial image and gait, because of their easy availability, are two preferable sources of biometric information analysis.
In this thesis, we investigated the development of most recent computer vision techniques and proposed various state-of-the-art models to solve the four principle problems in biometric information analysis including the age estimation, age progression, face retrieval and gait recognition.
For age estimation, the modeling has always been a challenge. Existing works model the age estimation problem as either a classiļ¬cation or a regression problem. However, these two types of models are not able to reveal the intrinsic nature of human age. To this end, we proposed a novel hierarchical framework and a ordinal metric learning based method. In the hierarchical framework, a random forest based clustering method is introduced to ļ¬nd an optimal age grouping protocol. In the ordinal metric learning approach, the age estimation is solved by learning an subspace where the ordinal structure of the data is preserved. Both of them have achieved state-of-the-art performance.
For face retrieval, speciļ¬cally under a cross-age setting, we ļ¬rst proposed a novel task, that is given two images, ļ¬nding the target image which is supposed to have the same identity with the ļ¬rst input and the same age with the second input. To tackle this task, we proposed a joint manifold learning method that can disentangle the identity with the age information. Accompanied with two independent similarity measurements, the retrieval can be easily performed.
For aging progression, we also proposed a novel task that has never been considered. We devoted to fuse the identity of one image with the age of another image. By proposing a novel framework based on generative adversarial networks, our model is able to generate close-to-realistic images.
Lastly, although gait recognition is an ideal long-distance biometric information task that makes up the shortfall of facial image, existing works are not able to handle large scale data with various view angles. We proposed a generative model to solve this term and achieved promising results. Moreover, our model is able to generate evidences for forensic usage
- ā¦