Hello Everyone. My name is Patrick Wande. I am a Computer Science Major at Earlham College with a strong interest in machine learning advancement and optimizations.
Facial recognition technology is gaining attention for its applications in security, surveillance, and identity verification. Optimizing facial recognition algorithms is essential for improving efficiency and accuracy. However, the computational complexity associated with these algorithms can impede real-time applications, necessitating optimization. This paper, therefore, proposes a comparative analysis of two optimization methodologies – Principal Component Analysis (PCA) and Harmonic Search Optimization (HSO) – on a commonly used facial recognition algorithm – the Elastic Bunch Graph Matching (EBGM) algorithm. EBGM, acknowledged for its ability to effectively capture facial structural information for robust recognition, faces limitations in real time applications due to its computationally intensive processes. To address this challenge, the study examines the potential of PCA, a common dimensionality reduction and feature extraction technique, in comparison to HSO, a metaheuristic optimization method. This project will analyze these techniques based on how well they improve EBGM’s recognition accuracy, computational time, and their ability to handle occluded facial images. The results will offer insights into optimizing EBGM, bridging the gap between a fundamental facial recognition techniques and the use of optimizations. This research contributes to knowledge and the development of efficient facial recognition algorithms.
Research Proposal (CS 388)
Below is the full proposal document for the discussed research: