Facial recognition using PCA

• المؤلف الرئيسي: Ngoh, Bernie Zhen Yuan
• مؤلفون آخرون: Chua Chin Seng
• التنسيق: Final Year Project
• اللغة: English
• منشور في: Nanyang Technological University 2020
• الموضوعات: Engineering::Electrical and electronic engineering
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/140026
• المؤسسة: Nanyang Technological University

The performance of facial recognition software has always suffered when it comes to unconstrained facial recognition. This is due to environmental variations such as changing illumination and occlusion, where part of the face is obstructed. Furthermore, faces in unconstrained facial recognition are usually tilted at different angles and not frontal facing. It is a challenge to perform recognition accurately while taking these variations into account. In this project, I explore the application of Principal Component Analysis in combination with an Artificial Neural Network using a triplet loss function to learn low-dimensional feature representations of facial images, known as face embeddings. K-Nearest Neighbours classifier will then be used to perform verification and identification tasks. This method yields an improvement in performance over the traditional Eigenfaces approach where only Principal Component Analysis is applied and Euclidean distances between data samples and test samples are compared for classification.