ROBUST FACIAL LANDMARKS LOCALIZATION WITH APPLICATIONS IN FACIAL BIOMETRICS

Localization of regions of interest on images and videos is a well studied prob- lem in computer vision community. Usually localization tasks imply localization of objects in a given image, such as detection and segmentation of objects in images. However, the regions of interests can be limited to a single pixel as in the task of facial landmark localization or human pose estimation. This dissertation studies ro- bust facial landmark detection algorithms for faces in the wild using learning methods based on Convolution Neural Networks. Detection of specific keypoints on face images is an integral pre-processing step in facial biometrics and numerous other applications including face verification and identification. Detecting keypoints allows to align face images to a canonical coordi- nate system using geometric transforms such as similarity or affine transformations mitigating the adverse affects of rotation and scaling. This challenging problem has become more attractive in recent years as a result of advances in deep learning and release of more unconstrained datasets. The research community is pushing bound-aries to achieve better and better performance on unconstrained images, where the images are diverse in pose, expression and lightning conditions. Over the years, researchers have developed various hand crafted techniques to extract meaningful features from features, most of them being appearance and geometry-based features. However, these features do not perform well for data col- lected in unconstrained settings due to large variations in appearance and other nui- sance factors. Convolution Neural Networks (CNNs) have become prominent because of their ability to extract discriminating features. Unlike the hand crafted features, DCNNs perform feature extraction and feature classification from the data itself in an end-to-end fashion. This enables the DCNNs to be robust to variations present in the data and at the same time improve their discriminative ability. In this dissertation, we discuss three different methods for facial keypoint de- tection based on Convolution Neural Networks. The methods are generic and can be extended to a related problem of keypoint detection for human pose estimation. The first method called Cascaded Local Deep Descriptor Regression uses deep features ex- tracted around local points to learn linear regressors for incrementally correcting the initial estimate of the keypoints. In the second method, called KEPLER, we develop efficient Heatmap CNNs to directly learn the non-linear mapping between the input and target spaces. We also apply different regularization techniques to tackle the effects of imbalanced data and vanishing gradients. In the third method, we model the spatial correlation between different keypoints using Pose Conditioned Convo- lution Deconvolution Networks (PCD-CNN) while at the same time making it pose agnostic by disentangling pose from the face image. Next, we show an applicationof facial landmark localization used to align the face images for the task of apparent age estimation of humans from unconstrained images. In the fourth part of this dissertation we discuss the impact of good quality landmarks on the task of face verification. Previously proposed methods perform with reasonable accuracy on high resolution and good quality images, but fail when the input image suffers from degradation. To this end, we propose a semi-supervised method which aims at predicting landmarks in the low quality images. This method learns to predict landmarks in low resolution images by learning to model the learning process of high resolution images. In this algorithm, we use Generative Adversarial Networks, which first learn to model the distribution of real low resolution images after which another CNN learns to model the distribution of heatmaps on the images. Additionally, we also propose another high quality facial landmark detection method, which is currently state of the art. Finally, we also discuss the extension of ideas developed for facial keypoint localization for the task of human pose estimation, which is one of the important cues for Human Activity Recognition. As in PCD-CNN, the parts of human body can also be modelled in a tree structure, where the relationship between these parts are learnt through convolutions while being conditioned on the 3D pose and orientation. Another interesting avenue for research is extending facial landmark localization to naturally degraded images

Automatic age progression and estimation from faces

Recently, automatic age progression has gained popularity due to its numerous applications. Among these is the frequent search for missing people, in the UK alone up to 300,000 people are reported missing every year. Although many algorithms have been proposed, most of the methods are affected by image noise, illumination variations, and facial expressions. Furthermore, most of the algorithms use a pattern caricaturing approach which infers ages by manipulating the target image and a template face formed by averaging faces at the intended age. To this end, this thesis investigates the problem with a view to tackling the most prominent issues associated with the existing algorithms. Initially using active appearance models (AAM), facial features are extracted and mapped to people’s ages, afterward a formula is derived which allows the convenient generation of age progressed images irrespective of whether the intended age exists in the training database or not. In order to handle image noise as well as varying facial expressions, a nonlinear appearance model called kernel appearance model (KAM) is derived. To illustrate the real application of automatic age progression, both AAM and KAM based algorithms are then used to synthesise faces of two popular long missing British and Irish kids; Ben Needham and Mary Boyle. However, both statistical techniques exhibit image rendering artefacts such as low-resolution output and the generation of inconsistent skin tone. To circumvent this problem, a hybrid texture enhancement pipeline is developed. To further ensure that the progressed images preserve people’s identities while at the same time attaining the intended age, rigorous human and machine based tests are conducted; part of this tests resulted to the development of a robust age estimation algorithm. Eventually, the results of the rigorous assessment reveal that the hybrid technique is able to handle all existing problems of age progression with minimal error.National Information Technology Development Agency of Nigeria (NITDA

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

EigenFIT : a statistical learning approach to facial composites

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Constructing 3D faces from natural language interface

This thesis presents a system by which 3D images of human faces can be constructed using a natural language interface. The driving force behind the project was the need to create a system whereby a machine could produce artistic images from verbal or composed descriptions. This research is the first to look at constructing and modifying facial image artwork using a natural language interface. Specialised modules have been developed to control geometry of 3D polygonal head models in a commercial modeller from natural language descriptions. These modules were produced from research on human physiognomy, 3D modelling techniques and tools, facial modelling and natural language processing. [Continues.

The Ocular Surface Control of Blinking, Tearing and Sensation

Thesis (Ph.D.) - Indiana University, Optometry, 2014Dry eye is a common condition that affects millions in the US and worldwide. It is considered to be a multifactorial disease of the tear film and ocular surface and is associated with symptoms of ocular discomfort and visual disturbance. Low blink rate has been identified as a potential risk factor for the development of dry eye because it can result in increased evaporative loss from the tear film. Failure of tear secretion has also been recognized as one of the main factors for dry eye development, characterized as low tear volume and slow tear turnover rate. Both factors in turn may lead to increased tear film hyperosmolarity and instability, which are considered core mechanisms of dry eye. In the natural condition, the ocular surface is mainly protected by blinking and tear secretion in that the newly secreted tears flow into the upper and lower meniscus and the blink spreads the new tear film from the meniscus to the ocular surface. Therefore, the ocular surface control over blinking and tear secretion is important in the etiology of the dry eye condition. In this proposal, we develop a laboratory model using human subjects to test how input from the ocular surface affects both blinking and tear secretion. We hypothesize that ocular surface stimuli will activate corneal receptors to signal a high blink rate, reflex tear secretion and ocular sensations of discomfort. These probably act together for the purpose of preventing ocular damage. These results will help us to understand the manner in which the ocular surface responds to adverse stimuli, which may ultimately lead toward further development of treatments or methods in dry eye patients