Individualised model of facial age synthesis based on constrained regression

YesFaces convey much information. Interestingly we humans have a remarkable ability of identifying, extracting, and interpreting this information. Recently automatic facial ageing (AFA) has gained popularity due to its numerous applications which include search for missing people, biometrics, and multimedia. The problem of AFA is faced with various challenges, including incomplete training datasets, unrestrained environments, ethnic and gender variations to mention but a few. This work presents a new approach to automatic facial ageing which involves the development of a person specific facial ageing system. A color based Active Appearance Model (AAM) is used to extract facial features. Then, regression is used to model an age estimator. Age synthesis is achieved by computing a solution that minimises the distance from the original face with the use of constrained regression. The model is tested on a challenging database of single image per person. Initial results suggest that plausible images can be rerendered at different ages, automatically using the AAM representation. Using the constrained regressor we are guaranteed to get estimated ages that are exact for an individual at a given age

On automatic age estimation from facial profile view

YesIn recent years, automatic facial age estimation has gained popularity due to its numerous applications. Much work has been done on frontal images and lately, minimal estimation errors have been achieved on most of the benchmark databases. However, in reality, images obtained in unconstrained environments are not always frontal. For instance, when conducting a demographic study or crowd analysis, one may get profile images of the face. To the best of our knowledge, no attempt has been made to estimate ages from the side-view of face images. Here we exploit this by using a pre-trained deep residual neural network (ResNet) to extract features. We then utilize a sparse partial least squares regression approach to estimate ages. Despite having less information as compared to frontal images, our results show that the extracted deep features achieve a promising performance

Facial age synthesis using sparse partial least squares (the case of Ben Needham)

YesAutomatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an Active Appearance Model (AAM) to extract facial features from available images. An ageing function is then modelled using Sparse Partial Least Squares Regression (sPLS). Thereafter, the ageing function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham’s facial image that was taken when he was 21 months old to the ages of 6, 14 and 22 years. The algorithm presented in this paper could potentially be used to enhance the search for missing people worldwide

Automatic real and apparent age estimation in still images

We performed a study on age estimation via still images creating a new face image database containing real age and apparent age label annotations. Two age estimation methods are proposed using the state of the art techniques and analyse their performance with the proposed database

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

Gland Instance Segmentation in Colon Histology Images

This thesis looks at approaches to gland instance segmentation in histology images. The aim is to find suitable local image representations to describe the gland structures in images with benign tissue and those with malignant tissue and subsequently use them for design of accurate, scalable and flexible gland instance segmentation methods. The gland instance segmentation is a clinically important and technically challenging problem as the morphological structure and visual appearance of gland tissue is highly variable and complex. Glands are one of the most common organs in the human body. The glandular features are present in many cancer types and histopathologists use these features to predict tumour grade. Accurate tumour grading is critical for prescribing suitable cancer treatment resulting in improved outcome and survival rate. Different cancer grades are reflected by differences in glands morphology and structure. It is therefore important to accurately segment glands in histology images in order to get a valid prediction of tumour grade. Several segmentation methods, including segmentation with and without pre-classification, have been proposed and investigated as part of the research reported in this thesis. A number of feature spaces, including hand-crafted and deep features, have been investigated and experimentally validated to find a suitable set of image attributes for representation of benign and malignant gland tissue for the segmentation task. Furthermore, an exhaustive experimental examination of different combinations of features and classification methods have been carried out using both qualitative and quantitative assessments, including detection, shape and area fidelity metrics. It has been shown that the proposed hybrid method combining image level classification, to identify images with benign and malignant tissue, and pixel level classification, to perform gland segmentation, achieved the best results. It has been further shown that modelling benign glands using a three-class model, i.e. inside, outside and gland boundary, and malignant tissue using a two-class model is the best combination for achieving accurate and robust gland instance segmentation results. The deep learning features have been shown to overall outperform handcrafted features, however proposed ring-histogram features still performed adequately, particularly for segmentation of benign glands. The adopted transfer-learning model with proposed image augmentation has proven very successful with 100% image classification accuracy on the available test dataset. It has been shown that the modified object- level Boundary Jaccard metric is more suitable for measuring shape similarity than the previously used object-level Hausdorff distance, as it is not sensitive to outliers and could be easily integrated with region- based metrics such as the object-level Dice index, as contrary to the Hausdorff distance it is bounded between 0 and 1. Dissimilar to most of the other reported research, this study provides comprehensive comparative results for gland segmentation, with a large collection of diverse types of image features, including hand-crafted and deep features. The novel contributions include hybrid segmentation model superimposing image and pixel level classification, data augmentation for re-training deep learning models for the proposed image level classification, and the object- level Boundary Jaccard metric adopted for evaluation of instance segmentation methods

A comparative evaluation of Regression Learning algorithms for Facial Age Estimation

The problem of automatic age estimation from facial images poses a great number of challenges: uncontrollable environment, insufficient and incomplete training data, strong person-specificity, and high within-range variance, among others. These difficulties have made researchers of the field propose complex and strongly hand-crafted descriptors, which make it difficult to replicate and compare the validity of posterior classification and regression schemes. We present a practi- cal evaluation of four machine learning regression techniques from some of the most representative families in age estimation: kernel techniques, ensemble learning, neural networks, and projection algorithms. Addition- ally, we propose the use of simple HOG descriptors for robust age estimation, which achieve comparable performance to the state-of-the-art, without requiring piecewise facial alignment through tens of landmarks, nor fine-tuned and specific modeling of facial aging, nor additional demo- graphic annotations such as gender or ethnicity. By using HOG descriptors, we discuss the benefits and drawbacks among the four learning algorithms. The accuracy and generalization of each regression technique is evaluated through cross-validation and cross-database validation over two large databases, MORPH and FRGC