Is this project an undergraduate, graduate, or faculty project?
Undergraduate
group
What campus are you from?
Daytona Beach
Authors' Class Standing
Kaitlyn Cavanaugh: Junior Adam Kuzmicki: Senior Brady Heddon: Senior Kristiyan Stefanov: Senior
Lead Presenter's Name
Brady Heddon
Faculty Mentor Name
Dr. Sirani M. Perera
Abstract
Facial recognition technology plays a crucial role in numerous fields, from national security to social media platforms. Convolutional Neural Networks (CNNs) stand out as the most prevalent technique for facial recognition due to their impressive accuracy and adaptability in various scenarios. However, CNN-based learning faces considerable challenges, including high computational demands, a dependence on large volumes of labeled data, and significant storage requirements, which all impede its overall efficiency.
We aim to implement an innovative and efficient facial recognition approach utilizing structured neural networks grounded in Discrete Cosine Transforms (DCTs), achieving accuracy comparable to conventional CNNs. Our extensive and varied facial dataset guarantees robustness against diverse features, expressions, and lighting conditions. Furthermore, we will conduct a comprehensive comparison between our proposed neural network and traditional methods such as Eigenfaces, emphasizing their performance in facial recognition applications.
Did this research project receive funding support from the Office of Undergraduate Research.
Yes, Student Internal Grant
An Efficient and Data-driven Learning Algorithm to Evaluate Facial Recognition
Facial recognition technology plays a crucial role in numerous fields, from national security to social media platforms. Convolutional Neural Networks (CNNs) stand out as the most prevalent technique for facial recognition due to their impressive accuracy and adaptability in various scenarios. However, CNN-based learning faces considerable challenges, including high computational demands, a dependence on large volumes of labeled data, and significant storage requirements, which all impede its overall efficiency.
We aim to implement an innovative and efficient facial recognition approach utilizing structured neural networks grounded in Discrete Cosine Transforms (DCTs), achieving accuracy comparable to conventional CNNs. Our extensive and varied facial dataset guarantees robustness against diverse features, expressions, and lighting conditions. Furthermore, we will conduct a comprehensive comparison between our proposed neural network and traditional methods such as Eigenfaces, emphasizing their performance in facial recognition applications.