On Body Mass Index Analysis from Human Visual Appearance

In the past few decades, overweight and obesity are spreading widely like an epidemic. Generally, a person is considered overweight by body mass index (BMI). In addition to a body fat measurement, BMI is also a risk factor for many diseases, such as cardiovascular diseases, cancers and diabetes, etc. Therefore, BMI is important for personal health monitoring and medical research. Currently, BMI is measured in person with special devices. It is an urgent demand to explore conveniently preventive tools. This work investigates the feasibility of analyzing BMI from human visual appearances, including 2-dimensional (2D)/3-dimensional (3D) body and face data. Motivated by health science studies which have shown that anthropometric measures, such as waist-hip ratio, waist circumference, etc., are indicators for obesity, we analyze body weight from frontal view human body images. A framework is developed for body weight analysis from body images, along with the computation methods of five anthropometric features for body weight characterization. Then, we study BMI estimation from the 3D data by measuring the correlation between the estimated body volume and BMIs, and develop an efficient BMI computation method which consists of body weight and height estimation from normally dressed people in 3D space. We also intensively study BMI estimation from frontal view face images via two key aspects: facial representation extracting and BMI estimator learning. First, we investigate the visual BMI estimation problem from the aspect of the characteristics and performance of different facial representation extracting methods by three designed experiments. Then we study visual BMI estimation from facial images by a two-stage learning framework. BMI related facial features are learned in the first stage. To address the ambiguity of BMI labels, a label distribution based BMI estimator is proposed for the second stage. The experimental results show that this framework improves the performance step by step. Finally, to address the challenges caused by BMI data and labels, we integrate feature learning and estimator learning in one convolutional neural network (CNN). A label assignment matching scheme is proposed which successfully achieves an improvement in BMI estimation from face images

Feedback Coding for Efficient Interactive Machine Learning

When training machine learning systems, the most basic scenario consists of the learning algorithm operating on a fixed batch of data, provided in its entirety before training. However, there are a large number of applications where there lies a choice in which data points are selected for labeling, and where this choice can be made “on the fly” after each selected data point is labeled. In such interactive machine learning (IML) systems, it is possible to train a model with far fewer labels than would be required with random sampling. In this thesis, we identify and model query structures in IML to develop direct information maximization solutions as well as approximations that allow for computationally efficient query selection. To do so, we frame IML as a feedback communications problem and directly apply principles and tools from coding theory to design and analyze new interaction selection algorithms. First, we directly apply a recently developed feedback coding scheme to sequential human-computer interaction systems. We then identify simplifying query structures to develop approximate methods for efficient, informative query selection in interactive ordinal embedding construction and preference learning systems. Finally, we combine the direct application of feedback coding with approximate information maximization to design and analyze a general active learning algorithm, which we study in detail for logistic regression.Ph.D

Musical timbre: bridging perception with semantics

Musical timbre is a complex and multidimensional entity which provides information regarding the properties of a sound source (size, material, etc.). When it comes to music, however, timbre does not merely carry environmental information, but it also conveys aesthetic meaning. In this sense, semantic description of musical tones is used to express perceptual concepts related to artistic intention. Recent advances in sound processing and synthesis technology have enabled the production of unique timbral qualities which cannot be easily associated with a familiar musical instrument. Therefore, verbal description of these qualities facilitates communication between musicians, composers, producers, audio engineers etc. The development of a common semantic framework for musical timbre description could be exploited by intuitive sound synthesis and processing systems and could even influence the way in which music is being consumed. This work investigates the relationship between musical timbre perception and its semantics. A set of listening experiments in which participants from two different language groups (Greek and English) rated isolated musical tones on semantic scales has tested semantic universality of musical timbre. The results suggested that the salient semantic dimensions of timbre, namely: luminance, texture and mass, are indeed largely common between these two languages. The relationship between semantics and perception was further examined by comparing the previously identified semantic space with a perceptual timbre space (resulting from pairwise dissimilarity rating of the same stimuli). The two spaces featured a substantial amount of common variance suggesting that semantic description can largely capture timbre perception. Additionally, the acoustic correlates of the semantic and perceptual dimensions were investigated. This work concludes by introducing the concept of partial timbre through a listening experiment that demonstrates the influence of background white noise on the perception of musical tones. The results show that timbre is a relative percept which is influenced by the auditory environment

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network