A Study of Face Embedding in Face Recognition

<p>Face Recognition has been a long-standing topic in computer vision and pattern recognition field because of its wide and important applications in our daily lives such as surveillance system, access control, and so on. The current modern face recognition model, which keeps only a couple of images per person in the database, can now recognize a face with high accuracy. Moreover, the model does not need to be retrained every time a new person is added to the database.</p> <p>By using the face dataset from Digital Democracy, the thesis will explore the capability of this model by comparing it with the standard convolutional neural network based on pose variations and training set sizes. First, we compare different types of pose to see their effect on the accuracy of the algorithm. Second, we train the system using different number of training images per person to see how many training samples are actually needed to maintain a reasonable accuracy.</p> <p>Finally, to push the limit, we decide to train the model using only a single image per person with the help of a face generation technique to synthesize more faces. The performance obtained by this integration is found to be competitive with the previous results, which are trained on multiple images.</p>

Name thesis:degree_name

MS in Computer Science

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2019

Author dc:creator

• Le, Khanh Duc

Contributors dc:contributor

• Xiaozheng (Jane) Zhang

• Neural Network
• Computer Vision
• Machine Learning
• Artificial Intelligence and Robotics
• Software Engineering

Identifier

10.15368/theses.2019.10

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3377