NORMA eResearch @NCI Library

Human Face Analysis using Transfer Learning Approach

Singh, Gaurav (2023) Human Face Analysis using Transfer Learning Approach. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (6MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (10MB) | Preview


The person’s face is among the most significant body parts for transmitting key features since it is so prominently shown. People of varying ages, sexes, and races each have a unique set of facial characteristics. Numerous studies have indicated that smokers have a significantly increased risk of experiencing substantial alterations in the middle and lower thirds of their faces. This research investigates the use of five distinct pre-trained - DCNN(deep convolutional neural network) learning models for identifying human face traits by making use of three different datasets and doing a comparative analysis. It is possible for governments, schools, insurance companies, and universities to leverage automation of large-scale BMI imputations, as well as age, gender, ethnicity, and smoking status, in order to collect data on a range of social concerns and make more informed decisions. Multiple studies have been done, with each one concentrating on a limited number of attributes and making use of a more condensed data set. In this particular investigation, pre-defined transfer learning networks such as EfficientNetB7, DenseNet201, InceptionResNetV2, VGG16, and ResNet50V2 were chosen over conventional-CNN (convolutional neural networks) in order to compare the results of the pre-trained models with one another. This comparison was carried out in order to determine which of the models performed the best. This was accomplished by employing a two-way comparison strategy. This study aims to quantify essential human face attributes and evaluate the findings based on parameters such as precision, accuracy, recall, RMSE, and MAE using a variety of methods that are considered to be state-of-the-art in the field.

Item Type: Thesis (Masters)
Basilio, Jorge
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
R Medicine > RA Public aspects of medicine
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Biometric Identification
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 26 May 2023 13:29
Last Modified: 26 May 2023 13:29

Actions (login required)

View Item View Item