Date of Graduation
12-2019
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Engineering
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
John Gauch
Committee Member/Reader
Pat Parkerson
Committee Member/Second Reader
Susan Gauch
Abstract
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 present brief descriptions of each study. We consider that this research will provide valuable insights and serve as a starting point for other students to apply deep learning approaches in their computer engineering and computer science studies.
Keywords
deep learning, face recognition, neural networks, node.js, javascript, html
Citation
Espinosa Sandoval, C. (2019). Multiple Face Detection and Recognition System Design Applying Deep Learning in Web Browsers using JavaScript. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/74