2 posts

Olin College: Quantitative Engineering Analysis

2021

Facial Recognition Using Principal Component Analysis

I have used multiple variations of Principal Component Analysis (PCA) in my research on microbial community analysis. To explain the core theory and assumptions of PCA to my lab group, I fleshed out an analysis of a toy example (eigenfaces) that I had originally seen in class. This analysis reasons about the assumptions of PCA and the effects of applying it to out-of-distribution data by using PCA to perform facial recognition on a dataset of my classmates’ faces.

I was invited to present this project at Łódź University’s SP2021 [virtual] MathUp conference at Łódź University, Poland. The conference was an incredibly fun opportunity to meet fellow researchers and data scientists!


1D and 2D Fourier Transforms

Concepts and math behind 1D and 2D discrete Fourier Transforms for signal and image analysis. Overview of mathematical steps, post-processing, assumptions, and reading of phase and magnitude plots.