On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Математичні моделі змагальних атак на системи розпізнавання образів

Робота складається з 3 розділів, містить 47 ілюстрацій, 1 таблиця, 32 літературних посилання, обсяг роботи - 102 сторінки. Завданням роботи є огляд різних змагальних атак на системи розпізнавання образів, вибір алгоритмів нейронних мереж для класифікації зображень, їх детальний опис та програмна реалізація на вибраних базах даних. Мета цієї дипломної роботи полягає у досліджені і реалізації змагальних атак на системи розпізнавання образів та огляд отриманих результатів. Об’єктом дослідження є процес реалізації змагальних атак на моделі розпізнавання образів. Предметом дослідження є алгоритми змагальних атак та моделей розпізнавання. Актуальність роботи зумовлюється тим, що на сьогоднішній день питання якісного розпізнавання образів є актуальним, як і побудова захисту таких систем. Методами дослідження дипломної роботи складають методи системного, порівняльного і статистичного аналізу, логіко-діалектичний метод пізнання, синтетичних та експертних оцінок, метод логічного узагальнення та синтезу. Вони базуються на використанні методів статистичного якісного і кількісного порівняння, наукової абстракції, факторного та структурного аналізу. Використано широке коло зарубіжних та вітчизняних літературних та електронних джерел. Наукова новизна одержаних результатів дослідження полягає в тому, що на підставі проведеного теоретико - методологічного аналізу побудовано модель змагальних атак на системи розпізнавання образів та показано їх вразливості, що можна використати для покращення систем захисту. Практичне застосування полягає в тому, що результати роботи можуть бути використані для побудови захищених моделей розпізнавання образів в різних установах.The work consists of 3 sections, contains 47 illustrations, 1 table, 32 literary references, the volume of the work is 102 pages. The task of the work is an overview of various adversarial attacks on pattern recognition systems, a selection of neural network algorithms for image classification, their detailed description and software implementation on selected databases. The purpose of this thesis is to research and implement adversarial attacks on pattern recognition systems and review the results. The object of research is the process of implementing adversarial attacks on pattern recognition models. The subject of research is the algorithms of adversarial attacks and recognition models. The relevance of the work is determined by the fact that today the issue of high-quality pattern recognition is relevant, as is the construction of protection for such systems. The research methods of the thesis include the methods of systematic, comparative and statistical analysis, the logical-dialectical method of cognition, synthetic and expert evaluations, the method of logical generalization and synthesis. They are based on the use of methods of statistical qualitative and quantitative comparison, scientific abstraction, factor and structural analysis. A wide range of foreign and domestic literary and electronic sources were used. The scientific novelty of the obtained research results is that, based on the theoretical and methodological analysis, a model of adversarial attacks on pattern recognition systems was built and their vulnerabilities were shown, which can be used to improve protection systems. The practical application is that the results of the work can be used to build secure pattern recognition models in various institutions

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end

Benchmark Analysis of Representative Deep Neural Network Architectures

This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity, computational complexity, memory usage, and inference time. The behavior of such performance indices and some combinations of them are analyzed and discussed. To measure the indices we experiment the use of DNNs on two different computer architectures, a workstation equipped with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson TX1 board. This experimentation allows a direct comparison between DNNs running on machines with very different computational capacity. This study is useful for researchers to have a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future; and for practitioners to select the DNN architecture(s) that better fit the resource constraints of practical deployments and applications. To complete this work, all the DNNs, as well as the software used for the analysis, are available online.Comment: Will appear in IEEE Acces