10 research outputs found
2DLDA-based texture recognition in the aspect of objective image quality assessment
The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition.It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression.Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method.The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database - Brodatz Textures.Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy
2DLDA-based texture recognition in the aspect of objective image quality assessment
The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition.It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression.Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method.The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database - Brodatz Textures.Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy
Principal Component Analysis
This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction
Biometric Liveness Detection Using Gaze Information
This thesis is concerned with liveness detection for biometric systems and in particular for face recognition systems. Biometric systems are well studied and have the potential to provide satisfactory solutions for a variety of applications.
However, presentation attacks (spoofng), where an attempt is made at subverting them system by making a deliberate presentation at the sensor is a serious challenge to their use in unattended applications. Liveness detection techniques can help with protecting biometric systems from attacks made through the presentation of artefacts and recordings at the sensor. In this work novel techniques for liveness detection are presented using gaze information.
The notion of natural gaze stability is introduced and used to develop a number of novel features that rely on directing the gaze of the user and establishing its behaviour. These features are then used to develop systems for detecting spoofng attempts. The attack scenarios considered in this work include the use of hand held photos and photo masks as well as video reply to subvert the system. The proposed features and systems based on them were evaluated extensively using data captured from genuine and fake attempts.
The results of the evaluations indicate that gaze-based features can be used to discriminate between genuine and imposter. Combining features through feature selection and score fusion substantially improved the performance of the proposed features
Hierarchical age estimation using enhanced facial features.
Doctor of Philosopy in Computer Science, University of KwaZulu-Natal, Westville, 2018.Ageing is a stochastic, inevitable and uncontrollable process that constantly affect
shape, texture and general appearance of the human face. Humans can easily determine
ones’ gender, identity and ethnicity with highest accuracy as compared to
age. This makes development of automatic age estimation techniques that surpass
human performance an attractive yet challenging task. Automatic age estimation
requires extraction of robust and reliable age discriminative features. Local binary
patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age
discriminative features. Although local ternary patterns (LTP) is insensitive to noise,
it uses a single static threshold for all images regardless of varied image conditions.
Local directional patterns (LDP) uses k directional responses to encode image gradient
and disregards not only central pixel in the local neighborhood but also 8 k
directional responses. Every pixel in an image carry subtle information. Discarding
8 k directional responses lead to lose of discriminative texture features. This
study proposes two variations of LDP operator for texture extraction. Significantorientation
response LDP (SOR-LDP) encodes image gradient by grouping eight
directional responses into four pairs. Each pair represents orientation of an edge
with respect to central reference pixel. Values in each pair are compared and the
bit corresponding to the maximum value in the pair is set to 1 while the other is
set to 0. The resultant binary code is converted to decimal and assigned to the central
pixel as its’ SOR-LDP code. Texture features are contained in the histogram of
SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the
difference between neighboring pixels and central pixel in 3 3 image region. These
differential values are convolved with Kirsch edge detectors to obtain directional
responses. These responses are normalized and used as probability of an edge occurring
towards a respective direction. An adaptive threshold is applied to derive
LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms
of negative and positive LTDP encoded images are concatenated to obtain
texture feature. Regardless of there being evidence of spatial frequency processing
in primary visual cortex, biologically inspired features (BIF) that model visual cortex
uses only scale and orientation selectivity in feature extraction. Furthermore,
these BIF are extracted using holistic (global) pooling across scale and orientations
leading to lose of substantive information. This study proposes multi-frequency BIF
(MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical
BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n
region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol,
this study investigated performance of proposed feature extractors in age estimation
in a hierarchical way by performing age-group classification using Multi-layer
Perceptron (MLP) followed by within age-group exact age regression using support
vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were
used to evaluate performance of proposed face descriptors. Experimental results on
FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform
state-of-the-art feature descriptors in age estimation. Experimental results show that
performing gender discrimination before age-group and age estimation further improves
age estimation accuracies. Shape, appearance, wrinkle and texture features
are simultaneously extracted by visual system in primates for the brain to process
and understand an image or a scene. However, age estimation systems in the literature
use a single feature for age estimation. A single feature is not sufficient enough
to capture subtle age discriminative traits due to stochastic and personalized nature
of ageing. This study propose fusion of different facial features to enhance their
discriminative power. Experimental results show that fusing shape, texture, wrinkle
and appearance result into robust age discriminative features that achieve lower
MAE compared to single feature performance
Advanced Information Systems and Technologies
This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science
Structured representation learning from complex data
This thesis advances several theoretical and practical aspects of the recently introduced restricted Boltzmann machine - a powerful probabilistic and generative framework for modelling data and learning representations. The contributions of this study represent a systematic and common theme in learning structured representations from complex data
Advanced Information Systems and Technologies
This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science
Symmetry-Adapted Machine Learning for Information Security
Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis