Computer Engineering Commons^™

Multiple Face Detection And Recognition System Design Applying Deep Learning In Web Browsers Using Javascript, Cristhian Gabriel Espinosa Sandoval

Computer Science and Computer Engineering Undergraduate Honors Theses

Deep learning has advanced progressively in the last years and now demonstrates state-of-the-art performance in various fields. In the era of big data, transformation of data into valuable knowledge has become one of the most important challenges in computing. Therefore, we will review multiple algorithms for face recognition that have been researched for a long time and are maturely developed, and analyze deep learning, presenting examples of current research.

To provide a useful and comprehensive perspective, in this paper we categorize research by deep learning architecture, including neural networks, convolutional neural networks, depthwise Separable Convolutions, densely connected convolutional networks, and …

Image-Driven Automated End-To-End Testing For Mobile Applications, Caleb Fritz

Computer Science and Computer Engineering Undergraduate Honors Theses

The increasing complexity and demand of software systems and the greater availability of test automation software is quickly rendering manual end-to-end (E2E) testing techniques for mobile platforms obsolete. This research seeks to explore the potential increase in automated test efficacy and maintainability through the use of computer vision algorithms when applied with Appium, a leading cross-platform mobile test automation framework. A testing framework written in a Node.js environment was created to support the development of E2E test scripts that examine and report the functional capabilities of a mobile test app. The test framework provides a suite of functions that connect …

Different Approaches To Blurring Digital Images And Their Effect On Facial Detection, Erich-Matthew Pulfer

Computer Science and Computer Engineering Undergraduate Honors Theses

The purpose of this thesis is to analyze the usage of multiple image blurring techniques and determine their effectiveness in combatting facial detection algorithms. This type of analysis is anticipated to reveal potential flaws in the privacy expected from blurring images or, rather, portions of images. Three different blurring algorithms were designed and implemented: a box blurring method, a Gaussian blurring method, and a differential privacy-based pixilation method. Datasets of images were collected from multiple sources, including the AT&T Database of Faces. Each of these three methods were implemented via their own original method, but, because of how common they …