Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158367
Title: Machine learning based face expression recognition
Authors: Paing Thu Thu Aung
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Paing Thu Thu Aung (2022). Machine learning based face expression recognition. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158367
Project: P3042-202 
Abstract: Face expression recognition is an active research area in the past two decades. Many attempts have been made to understand how human beings perceive human faces. It is widely accepted that face recognition may rely on both componential cues (such as eyes, mouth, nose, and cheeks) and non componential/holistic cues (considering the face as whole rather than as separate parts). However, how these cues should be optimally integrated remains unclear. Most state-of-the-art technologies of face expression recognition employ either componential cues or holistic information. Their recognition performance is therefore limited. This project investigates ways to integrate componential and holistic cues. We deployed a pretrained facial landmark detector to locate 68 landmarks of a face, to extract 8 individual facial components. Next, we utilized a convolutional network (CNN) to extract and learn relevant features from the facial and 8 componential images. Moreover, we deployed a CatBoost classifier to classify the landmark coordinates. Finally, we deployed soft and hard voting to combine all the predictions of the 10 trained models together. The soft voting approach achieved an accuracy of 63.87%, which is comparable to some existing method, considering we deployed fewer data for training. The creative approach may potentially lead to a better face expression recognition technology that outperforms current existing methods.
URI: https://hdl.handle.net/10356/158367
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Sally_FYPReport.pdf
  Restricted Access
U1820473L FYP Report2.75 MBAdobe PDFView/Open

Page view(s)

64
Updated on Dec 6, 2022

Download(s) 50

22
Updated on Dec 6, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.