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

Computational Intelligence in Automatic Face Age Estimation: A Survey

With the rapid growth of computational intelligence techniques, automatic face age estimation has achieved good accuracy that benefited real-world applications such as access control and monitoring, soft biometrics, and information retrieval. Over the past decade, many new algorithms were developed and previous surveys on face age estimation were either outdated or incomplete. Considering the importance of the expanding research in this topic, we aim to provide an up-to-date survey on the face age estimation techniques. First, we summarize the state-of-the-art databases and the performance metrics for face age estimation. Then, we review the age estimation techniques based on three categories of face features (local, global, and hybrid) and discuss different types of age learning algorithms. Finally, we identify the challenges and provide new insights for future research directions of fully automated face age estimation

Genetic risk factors for stroke-related quantitative traits and their associated ischaemic stroke subtypes

Stroke is the 2nd leading cause of death in the UK and worldwide. 150,000 people have a stroke each year in the UK (ischaemic stroke being the most common) and a significant proportion of NHS resources go towards the treatment of these individuals (~£2.8 billion). Twin and family history studies have shown that having affected relatives makes you between 30 and 76% more likely to suffer a stroke, suggesting that there is a genetic component to the disease. So far, no genes have been convincingly associated with stroke. Intermediate traits may be useful tools for identifying genetic factors in complex disease. For stroke, two commonly used intermediate traits are carotid intima-media thickness (CIMT) and white matter hyperintensities (WMHs), which both show high heritabilities. These traits have both been studied widely for associations with many candidate gene polymorphisms. In this thesis I systematically reviewed the literature for all genetic association studies of these two traits. Where particular associations have been studied in large numbers I meta-analysed the available data, developing novel methods for meta-analysis of genetic association data. I found there was substantial heterogeneity and small study bias in the literature and most polymorphisms have still been studied in too small numbers to make accurate conclusions. Apolipoprotein E (APOE) ε is the only polymorphism which shows a consistent association with CIMT, even when only the largest studies are analysed (MD 8μm (95% CI 6 to 11) between E4 and E3, and E3 and E2). No polymorphism has shown a convincing association with WMHs and interestingly APOE appears unlikely to be associated with this trait. This is consistent with previous work that shows that APOE is associated with large artery but not small artery stroke. Taking this hypothesis I attempted to investigate the association of APOE comparing patients who have had a large artery stroke with those who have had a small artery stroke in the Edinburgh Stroke Study cohort. However, genotyping of this polymorphism failed and I present investigatory analyses of problems from the genotyping laboratory

Automatic extraction of retinal features to assist diagnosis of glaucoma disease

Glaucoma is a group of eye diseases that have common traits such as high eye pressure, damage to the Optic Nerve Head (ONH) and gradual vision loss. It affects the peripheral vision and eventually leads to blindness if left untreated. The current common methods of diagnosis of glaucoma are performed manually by the clinicians. Clinicians perform manual image operations such as change of contrast, zooming in zooming out etc to observe glaucoma related clinical indications. This type of diagnostic process is time consuming and subjective. With the advancement of image and vision computing, by automating steps in the diagnostic process, more patients can be screened and early treatment can be provided to prevent any or further loss of vision. The aim of this work is to develop a system called Glaucoma Detection Framework (GDF), which can automatically determine changes in retinal structures and imagebased pattern associated with glaucoma so as to assist the eye clinicians for glaucoma diagnosis in a timely and effective manner. In this work, several major contributions have been made towards the development of the automatic GDF consisting of the stages of preprocessing, optic disc and cup segmentation and regional image feature methods for classification between glaucoma and normal images. Firstly, in the preprocessing step, a retinal area detector based on superpixel classification model has been developed in order to automatically determine true retinal area from a Scanning Laser Ophthalmoscope (SLO) image. The retinal area detector can automatically extract artefacts out from the SLO image while preserving the computational effciency and avoiding over-segmentation of the artefacts. Localization of the ONH is one of the important steps towards the glaucoma analysis. A new weighted feature map approach has been proposed, which can enhance the region of ONH for accurate localization. For determining vasculature shift, which is one of glaucoma indications, we proposed the ONH cropped image based vasculature classification model to segment out the vasculature from the ONH cropped image. The ONH cropped image based vasculature classification model is developed in order to avoid misidentification of optic disc boundary and Peripapillary Atrophy (PPA) around the ONH of being a part of the vasculature area. Secondly, for automatic determination of optic disc and optic cup boundaries, a Point Edge Model (PEM), a Weighted Point Edge Model (WPEM) and a Region Classification Model (RCM) have been proposed. The RCM initially determines the optic disc region using the set of feature maps most suitable for the region classification whereas the PEM updates the contour using the force field of the feature maps with strong edge profile. The combination of PEM and RCM entitled Point Edge and Region Classification Model (PERCM) has significantly increased the accuracy of optic disc segmentation with respect to clinical annotations around optic disc. On the other hand, the WPEM determines the force field using the weighted feature maps calculated by the RCM for optic cup in order to enhance the optic cup region compared to rim area in the ONH. The combination of WPEM and RCM entitled Weighted Point Edge and Region Classification Model (WPERCM) can significantly enhance the accuracy of optic cup segmentation. Thirdly, this work proposes a Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from the existing methods focusing on global features information only, our approach after optic disc localization and segmentation can automatically divide an image into five regions (i.e. optic disc or Optic Nerve Head (ONH) area, inferior (I), superior(S), nasal(N) and temporal(T)). These regions are usually used for diagnosis of glaucoma by clinicians through visual observation only. It then extracts image-based information such as textural, spatial and frequency based information so as to distinguish between normal and glaucoma images. The method provides a new way to identify glaucoma symptoms without determining any geometrical measurement associated with clinical indications glaucoma. Finally, we have accommodated clinical indications of glaucoma including the CDR, vasculature shift and neuroretinal rim loss with the RIFM classification and performed automatic classification between normal and glaucoma images. Since based on the clinical literature, no geometrical measurement is the guaranteed sign of glaucoma, the accommodation of the RIFM classification results with clinical indications of glaucoma can lead to more accurate classification between normal and glaucoma images. The proposed methods in this work have been tested against retinal image databases of 208 fundus images and 102 Scanning Laser Ophthalmoscope (SLO) images. These databases have been annotated by the clinicians around different anatomical structures associated with glaucoma as well as annotated with healthy or glaucomatous images. In fundus images, ONH cropped images have resolution varying from 300 to 900 whereas in SLO images, the resolution is 341 x 341. The accuracy of classification between normal and glaucoma images on fundus images and the SLO images is 94.93% and 98.03% respectively

Gender classification using facial components.

Master’s degree. University of KwaZulu-Natal, Durban.Gender classification is very important in facial analysis as it can be used as input into a number of systems such as face recognition. Humans are able to classify gender with great accuracy however passing this ability to machines is a complex task because of many variables such as lighting to mention a few. For the purpose of this research we have approached gender classification as a binary problem, involving the two classes male and female. Two datasets are used in this research which are the FG-NET dataset and Pilots Parliament datasets. Two appearance based feature extractors are used which are the LBP and LDP with the Active Shape model being included by fusing. The classifiers used here are the Support Vector Machine with Radial Basis Function kernel and an Artificial Neural Network with backpropagation. On the FG-NET an average detection of 90.6% against that of 87.5% to that of the PPB. Gender is then detected from the facial components the nose, eyes among others. The forehead recorded the highest accuracy with 92%, followed by the nose with 90%, cheeks with 89.2% and the eyes with 87% and the mouth recorded the lowest accuracy of 75%. As a result feature fusion is then carried out to improve classification accuracies especially that of the mouth and eyes with lowest accuracies. The eyes with an accuracy of 87% is fused with the forehead with 92% and the resulting accuracy is an increase to 93%. The mouth, with the lowest accuracy of 75% is fused with the nose which has an accuracy of 90% and the resulting accuracy is 87%. These results carried out by fusing through addition showed improved results. Fusion is then carried out between Appearance based and shape based features. On the FG-NET dataset using the LBP and LDP an accuracy of 85.33% and 89.53% with the PPB recording 83.13%, 89.3% for LBP and LDP respectively. As expected and shown by previous researchers the LDP clearly obtains higher classification accuracies as it than LBP as it uses gradient rather than pixel intensity. We then fuse the vectors of the LDP, LBP with that of the ASM and carry out dimensionality reduction, then fusion by addition. On the PPB dataset fusion of LDP and ASM records 81.56%, and 94.53% with the FG-NET recording 89.53% respectively

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research

Socio-demographic factors, smoking, symptoms, morbidities and pulmonary function and quality of life in individuals with a heavy smoking history

Objective: To determine which socio-demographic, exposure, morbidity and symptom variables are associated with health-related quality of life among former and current heavy smokers. Methods: Cross sectional data from 2537 participants were studied. All participants were at ≥2% risk of developing lung cancer within 6 years. Linear and logistic regression models utilizing a multivariable fractional polynomial selection process identified variables associated with health-related quality of life, measured by the EQ-5D. Results: Upstream and downstream associations between smoking cessation and higher health-related quality of life were evident. Significant upstream associations, such as education level and current working status and were explained by the addition of morbidities and symptoms to regression models. Having arthritis, decreased forced expiratory volume in one second, fatigue, poor appetite or dyspnea were most highly and commonly associated with decreased HRQoL. Discussion: Upstream factors such as educational attainment, employment status and smoking cessation should be targeted to prevent decreased health-related quality of life. Practitioners should focus treatment on downstream factors, especially symptoms, to improve health-related quality of life

Aerospace medicine and biology A continuing bibliography with indexes, Oct. 1965

Bibliography of aerospace medicine and biolog

New approaches to unveil the Transcriptional landscape of dopaminergic neurons

Recent advances in studying the mammalian transcriptome arised new questions about how genes are organized and what is the function of noncoding RNAs. Furthermore, the discovery of large amounts of polyA- transcripts and antisense transcription proved that a portion of the transcriptome has still to be characterized. The complex anatomo-functional organization of the brain has prevented a comprehensive analysis of the transcriptional landscape of this tissue. New techniques must be developed to approach neuronal heterogeneity. In this study we combined Laser Capture Microdissection (LCM) and nanoCAGE, based on Cap Analysis of Gene Expression (CAGE), to describe expressed genes and map their transcription start sites (TSS) in two specific populations, A9 and A10, of mouse mesencephalic dopaminergic cells. Although sharing common dopaminergic marker genes, these two populations are part of different midbrain anatomical structures, substantia nigra (SN) for A9 and ventral tegmental area (VTA) for A10, project to relatively distinct areas, participate to distinct ascending dopaminergic pathways, exhibit different electrophysiological properties and different susceptibility to neurodegeneration in Parkinson`s disease. Specific neurons were identified by the expression of Green Fluorescent Protein driven by a celltype specific promoter in transgenic mice. High-quality RNAs were purified from 1000-2500 cells collected by LCM. We adapted the CAGE technique to analyze limiting amounts of RNAs (nanoCAGE). We took advantage of the cap-switching properties of the reverse transcriptase to specifically tag the 5`end of transcripts with a sequence containing a class III restriction site for EcoP15I. By creating 32bp 5`tags, we considerably improved the TSS mapping rate on the genome. A semi-suppressive PCR strategy was used to prevent primer dimers formation. The use of random priming in the 1st strand synthesis allowed to capture poly(A)- RNAs. 5`tags were sequenced with Illumina-Solexa platform. Here we show that this new nanoCAGE technology ensures a true high-throughput coverage of the transcriptome of a small number of identified neurons and can be used as an effective mean for gene discovery in the noncoding RNAs, to uncover putative alternative promoters associated to variants of protein coding transcripts and to detect potentially regulatory antisense transcripts. A further experimental validation by 5`RACE (Rapid Amplification of cDNA Ends) and RT-PCR on few candidate genes, have confirmed the existence in vivo of alternative TSS in the case of key regulatory genes involved in specifying and maintaining the dopaminergic phenotype of these neurons such as \u3b1-synuclein (Snca), dopamine transporter (Dat), vescicular monoamine transporter 2 (Vmat2), catechol-O-methyltransferase (Comt). Furthermore the differential expression of an antisense transcript overlapping to the polyubiquitin (Ubc) gene was detected as potentially interesting candidate gene accounting for differences in the ubiquitin-proteasome system (UPS) function in the two neuron populations. The potential implications deriving from these newly discovered alternative promoters and transcripts are discussed, considering also the potential consequences for the corresponding protein isoforms