Khóa luận tốt nghiệp xây dựng hệ thống nhận diện người nổi tiếng ở VIỆT NAM cho mạng xã hội LOTUS
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ĐẠI HỌC QUỐC GIA TP. HỒ CHÍ MINH
TRƯỜNG ĐẠI HỌC CÔNG NGHỆ THÔNG TIN
KHOA CÔNG NGHỆ PHẦN MỀM
LÊ THỊ PHƯƠNG NGÂN
NGUYẾN TIẾN TRUNG
KHÓA LUẬN TỐT NGHIỆP
XÂY DỰNG HỆ THỐNG NHẬN DIỆN NGƯỜI NỔI
TIẾNG Ở VIỆT NAM CHO MẠNG XÃ HỘI LOTUS
BUILDING VIETNAMESE CELEBRITY FACE RECOGNITION
SYSTEM FOR LOTUS - VIETNAM SOCIAL NETWORK
KỸ SƯ NGÀNH KỸ THUẬT PHẦN MỀM
TP. HỒ CHÍ MINH, 2021
VIETNAM NATIONAL UNIVERSITY - HO CHI MINH CITY
UNIVERSITY OF INFORMATION TECHNOLOGY
SOFTWARE ENGINEERING
UNDERGRADUATE THESIS
BUILDING VIETNAMESE CELEBRITY FACE
RECOGNITION SYSTEM FOR LOTUS - VIETNAM
SOCIAL NETWORK
INSTRUCTORS:
M.S DO VAN TIEN
STUDENTS:
LE THI PHUONG NGAN - 16520792
NGUYEN TIEN TRUNG - 16521321
Ho Chi Minh City, 2021
THƠNG TIN HỘI ĐỒNG CHẤM KHĨA LUẬN TỐT NGHIỆP
Hội đồng chấm khóa luận tốt nghiệp, thành lập theo Quyết định số ……………………
ngày ………………….. của Hiệu trưởng Trường Đại học Công nghệ Thông tin.
1. …………………………………………. – Chủ tịch.
2. …………………………………………. – Thư ký.
3. …………………………………………. – Ủy viên.
4. …………………………………………. – Ủy viên.
ĐẠI HỌC QUỐC GIA TP. HỒ CHÍ MINH
CỘNG HỊA XÃ HỘI CHỦ NGHĨA VIỆT NAM
TRƯỜNG ĐẠI HỌC
Độc Lập - Tự Do - Hạnh Phúc
CÔNG NGHỆ THÔNG TIN
TP. HCM, ngày…..tháng…..năm……..
NHẬN XÉT KHĨA LUẬN TỐT NGHIỆP
(CỦA CÁN BỘ HƯỚNG DẪN/PHẢN BIỆN)
Tên khóa luận:
XÂY DỰNG HỆ THỐNG NHẬN DIỆN NGƯỜI NỔI TIẾNG Ở VIỆT NAM CHO MẠNG
XÃ HỘI LOTUS
Nhóm SV thực hiện:
Cán bộ hướng dẫn/phản biện:
Lê Thị Phương Ngân
16520792
Nguyễn Tiến Trung
16521321
Ths. Đỗ Văn Tiến
Đánh giá Khóa luận
1. Về cuốn báo cáo:
Số trang
Số bảng số liệu
Số tài liệu tham khảo
_______
_______
_______
Số chương
Số hình vẽ
Sản phẩm
Một số nhận xét về hình thức cuốn báo cáo:
2. Về nội dung nghiên cứu:
_______
_______
_______
3. Về chương trình ứng dụng:
4. Về thái độ làm việc của sinh viên:
Đánh giá chung:Khóa luận đạt/khơng đạt u cầu của một khóa luận tốt nghiệp kỹ sư/ cử nhân,
xếp loại Giỏi/ Khá/ Trung bình
Điểm từng sinh viên:
Lê Thị Phương Ngân: ………../10
Nguyễn Tiến Trung: ………../10
Người nhận xét
(Ký tên và ghi rõ họ tên)
ĐỖ VĂN TIẾN
ĐẠI HỌC QUỐC GIA TP. HỒ CHÍ MINH
CỘNG HỊA XÃ HỘI CHỦ NGHĨA VIỆT NAM
TRƯỜNG ĐẠI HỌC
Độc Lập - Tự Do - Hạnh Phúc
CÔNG NGHỆ THÔNG TIN
TP. HCM, ngày…..tháng…..năm……..
NHẬN XÉT KHĨA LUẬN TỐT NGHIỆP
(CỦA CÁN BỘ PHẢN BIỆN)
Tên khóa luận:
XÂY DỰNG HỆ THỐNG NHẬN DIỆN NGƯỜI NỔI TIẾNG Ở VIỆT NAM CHO MẠNG
XÃ HỘI LOTUS
Nhóm SV thực hiện:
Cán bộ hướng dẫn/phản biện:
Lê Thị Phương Ngân
16520792
Nguyễn Tiến Trung
16521321
Đánh giá Khóa luận
1. Về cuốn báo cáo:
Số trang
Số bảng số liệu
Số tài liệu tham khảo
_______
_______
_______
Số chương
Số hình vẽ
Sản phẩm
Một số nhận xét về hình thức cuốn báo cáo:
2. Về nội dung nghiên cứu:
_______
_______
_______
3. Về chương trình ứng dụng:
4. Về thái độ làm việc của sinh viên:
Đánh giá chung:Khóa luận đạt/khơng đạt u cầu của một khóa luận tốt nghiệp kỹ sư/ cử nhân,
xếp loại Giỏi/ Khá/ Trung bình
Điểm từng sinh viên:
Lê Thị Phương Ngân: ………../10
Nguyễn Tiến Trung: ………../10
Người nhận xét
(Ký tên và ghi rõ họ tên)
We would like to give our thesis for those who always help and teach us
useful knowledge during the time we complete our thesis, for those who always inspire us when we face difficult problems and for our beloved family
who always facilitate us to complete our entire study.
ACKNOWLEDGEMENTS
Firstly, we specially would like to show our appreciation and thank M.S Do
Van Tien so much for instructing, helping and making pieces of useful advice for us. The conscientious instructors have taught us a lot of knowledge,
along with various skills to complete our undergraduate thesis.
Lastly, we thank our family and friends of the KTPM2016 class for always
inspiring us through the time we studied at University of Information Technology.
Ho Chi Minh City, 1 - 2021
Le Thi Phuong Ngan - Nguyen Tien Trung
Contents
Contents
iii
List of Figures
vii
List of Tables
x
Nomenclature
xi
1
INTRODUCTION
3
1.1
Problem statements . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.2
Goals and scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
1.2.1
Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
1.2.2
Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
1.3
Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
1.4
Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
2
RELATED WORKS
8
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
2.2
Image processing face recognition . . . . . . . . . . . . . . . . . . .
9
2.3
The methods face recognition . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1
Classical face recognition algorithms . . . . . . . . . . . . . 10
2.3.2
Deep learning for face recognition . . . . . . . . . . . . . . . 11
2.4
Face recognition applications . . . . . . . . . . . . . . . . . . . . . . 11
2.5
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
iii
CONTENTS
3
FACE RECOGNITION ON DEEP LEARNING
3.1
3.2
3.3
3.4
4
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1.1
Artificial Neural Networks (ANNs) . . . . . . . . . . . . . . 13
3.1.2
Convolutional Neural Networks (CNNs) . . . . . . . . . . . . 17
Face Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2.1
MTCNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2.2
SSH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2.3
Retinaface . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.1
Softmax Loss . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.2
Centre Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.3
Triplet Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.4
SphereFace Loss . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3.5
CosFace Loss . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.6
Arcface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Searching Embedding Vector . . . . . . . . . . . . . . . . . . . . . . 34
3.4.1
Similarity Searching . . . . . . . . . . . . . . . . . . . . . . 34
3.4.2
Evaluating similarity search . . . . . . . . . . . . . . . . . . 35
3.4.3
Faiss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
EXPERIMENT
4.1
4.2
13
36
Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.1.1
Create celebrity list . . . . . . . . . . . . . . . . . . . . . . . 36
4.1.2
Preprocess data . . . . . . . . . . . . . . . . . . . . . . . . . 38
Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.1
Some evaluation measures . . . . . . . . . . . . . . . . . . . 38
4.2.1.1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.1.2
Precision and Recall . . . . . . . . . . . . . . . . . 39
4.2.1.3
F1-Score . . . . . . . . . . . . . . . . . . . . . . . 39
4.2.1.4
IoU . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2.1.5
mAP . . . . . . . . . . . . . . . . . . . . . . . . . 41
iv
CONTENTS
4.3
Results and Evalutions . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.3.1
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.3.1.1
4.3.2
4.4
5
Evalutions . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
DEMONSTRATION
44
5.1
Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2
Use-case diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2.1
Actor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2.2
Usecase diagram . . . . . . . . . . . . . . . . . . . . . . . . 45
5.2.3
Usecase Specification . . . . . . . . . . . . . . . . . . . . . . 46
5.2.3.1
The use case description celebrity identity . . . . . 46
5.2.3.2
The use case description looking for celebrity . . . 47
5.2.3.3
The use case description add celebrity . . . . . . . 48
5.3
Sequence diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.4
Activity diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.5
Processing flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.6
System architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.6.1
5.7
The celebrity recognition system API in Vietnam . . . . . . . 57
Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.7.1
6
Face detection stage . . . . . . . . . . . . . . . . . 41
Screen Details Description . . . . . . . . . . . . . . . . . . . 59
5.7.1.1
Home Page. . . . . . . . . . . . . . . . . . . . . . 59
5.7.1.2
Predict Page. . . . . . . . . . . . . . . . . . . . . . 61
5.7.1.3
Search Page. . . . . . . . . . . . . . . . . . . . . . 63
5.7.1.4
Celebrity Add Page. . . . . . . . . . . . . . . . . . 64
5.8
Consequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.9
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
CONCLUSION AND DEVELOPMENT
6.1
67
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
v
CONTENTS
6.2
Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
References
69
vi
List of Figures
1.1
Some celebrity people in Vietnam. . . . . . . . . . . . . . . . . . . .
4
2.1
Face recognition process flow.1 . . . . . . . . . . . . . . . . . . . . .
9
2.2
A mesh consists of vertices plus triangles . . . . . . . . . . . . . . . 10
2.3
A mesh consists of vertices plus triangles . . . . . . . . . . . . . . . 11
3.1
The illustration of a simple architecture of Artificial neural network. . 13
3.2
A cartoon drawing of a biological neuron (left) and a common mathematical model (right). . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3
Sigmoid function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.4
Tanh function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.5
ReLU function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.6
Activation function of ANN. . . . . . . . . . . . . . . . . . . . . . . 16
3.7
An example of the receptive field. . . . . . . . . . . . . . . . . . . . 18
3.8
The ReLU activation function. . . . . . . . . . . . . . . . . . . . . . 19
3.9
An example for fully connected layer (FC). . . . . . . . . . . . . . . 20
3.10 Pipeline of cascaded framework . . . . . . . . . . . . . . . . . . . . 21
3.11 The architectures of P-Net . . . . . . . . . . . . . . . . . . . . . . . 21
3.12 The architectures of R-Net . . . . . . . . . . . . . . . . . . . . . . . 22
3.13 The architectures of O-net . . . . . . . . . . . . . . . . . . . . . . . 22
3.14 The network architecture of SSH . . . . . . . . . . . . . . . . . . . . 23
3.15 Detection Module : Set of conv layers for detecting and localizing faces 23
3.16 Context Module: Used by Detection Module . . . . . . . . . . . . . . 24
3.17 Face localisation tasks from coarse to fine . . . . . . . . . . . . . . . 26
3.18 A mesh consists of vertices plus triangles . . . . . . . . . . . . . . . 26
vii
LIST OF FIGURES
3.19 Common loss functions for Face Recognition: Softmax Loss . . . . . 28
3.20 Decision margins of Softmax loss function under binary classification
case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.21 Based on the centre and feature normalisation, all identities are distributed on a hyperspher. . . . . . . . . . . . . . . . . . . . . . . . . 29
3.22 Triplet loss example . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.23 Feometry Interpretation of Euclidean margin loss. . . . . . . . . . . . 30
3.24 Decision margins of different loss functions under binary classification case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.25 Training a DCNN for face recognition supervised by the ArcFace loss
32
3.26 Toy examples under the Softmax and ArcFace loss on 8 identities
with 2D features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.1
Query list data from . . . . . . . . . . . . 37
4.2
Crawl image for Son Tung MTP singer from selenium . . . . . . . . 38
4.3
Illustration for Confusion matrix (Internet) . . . . . . . . . . . . . . . 39
4.4
Formula Precision and Recall (Internet) . . . . . . . . . . . . . . . . 39
4.5
Formula F1-Score (Internet) . . . . . . . . . . . . . . . . . . . . . . 40
4.6
Illustration for IoU (Internet) . . . . . . . . . . . . . . . . . . . . . . 40
4.7
Formula mAP (Internet) . . . . . . . . . . . . . . . . . . . . . . . . 41
4.8
Representation formula mAP (Internet) . . . . . . . . . . . . . . . . 41
4.9
Statistical chart mAP . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.10 Statistical chart Average inference time . . . . . . . . . . . . . . . . 42
5.1
Usecase diagram1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.2
Sequence diagram Celebrity identity. . . . . . . . . . . . . . . . . . . 49
5.3
Sequence diagram looking for celebrity. . . . . . . . . . . . . . . . . 50
5.4
Sequence diagram add celebrity. . . . . . . . . . . . . . . . . . . . . 51
5.5
Activity diagram celebrity identity. . . . . . . . . . . . . . . . . . . . 52
5.6
Activity diagram looking for celebrity. . . . . . . . . . . . . . . . . . 53
5.7
Activity diagram add celebrity. . . . . . . . . . . . . . . . . . . . . . 54
5.8
Processing flow system. . . . . . . . . . . . . . . . . . . . . . . . . . 55
viii
LIST OF FIGURES
5.9
Client-server architecture. . . . . . . . . . . . . . . . . . . . . . . . . 56
5.10 The face recognition celebrity system architecture. . . . . . . . . . . 57
5.11 Screens flow diagram of the application that celebrity recognition in
images.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.12 Home Page.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.13 Predict Page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.14 Search Page. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.15 Celebrity Add Page Steps 1. . . . . . . . . . . . . . . . . . . . . . . 64
5.16 Celebrity Add Page Steps 2. . . . . . . . . . . . . . . . . . . . . . . 65
ix
List of Tables
3.1
Verification performance (%) of different methods on LFW and YTF . 34
4.1
Celebrity data statistics . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.1
List actor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2
Table usecase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.3
Input paramenter API: /getcelebs . . . . . . . . . . . . . . . . . . . . 57
5.4
Results API: /getcelebs . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.5
Input paramenter API: /predict . . . . . . . . . . . . . . . . . . . . . 58
5.6
Results API: /predict . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.7
Input paramenter API: /search . . . . . . . . . . . . . . . . . . . . . 58
5.8
Results API: /search . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.9
Input paramenter API: /checkname . . . . . . . . . . . . . . . . . . . 59
5.10 Results API: /checkname . . . . . . . . . . . . . . . . . . . . . . . . 59
5.11 Input paramenter API: /addceleb . . . . . . . . . . . . . . . . . . . . 59
5.12 Results API: /addceleb . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.13 Table List Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.14 Parameters in Home Page . . . . . . . . . . . . . . . . . . . . . . . . 62
5.15 Parameters in Predict Page . . . . . . . . . . . . . . . . . . . . . . . 62
5.16 Parameters in Search Page . . . . . . . . . . . . . . . . . . . . . . . 63
5.17 Parameters in Celebrity Add Page . . . . . . . . . . . . . . . . . . . 65
x
Nomenclature
Abbreviations
ANN(s) Artificial Neural Networks
CNN(s) Convolutional Neural Networks
Conv Convolutional
LBP Local binary patterns
LMCL Large Margin Cosine Loss
MTCNN Multi-task Cascaded Convolutional Networks
NMS Non-maximum suppression
PCA Principal component analysi
ReLU Rectified Linear Unit Layer
SSH Single Stage Headless
SV M Support Vector Machine
xi
ABSTRACT
With the development of technology and engineering, computer fields are
increasingly developed, especially in the field of computer vision. One of
the problems in computer vision is face recognition. Face recognition has
many applications in many different fields: attendance, identification, security, etc.
There are many research and methods for the face recognition problem in
images. However, The methods applied primarily deal with images containing faces that are oriented, vertically oriented, and well-lit. In the real problem, images need to be processed because they are subjected to many environmental impacts and many different distortions. Especially with celebrity
recognition problems, the recognition and classification of celebrity people
in a country are very complicated. So the group decided to research and
solve the problem of recognizing the faces of celebrity people in Vietnam.
In the field of computer vision, there are quite a few methods to solve these
problems, especially the current approaches that are supposed to achieve
good results are the use of deep learning. Choosing the precision and processing speed to build the application is also a big challenge for us.
In consequence, this thesis has done the following contents:
• Get an overview of machine learning, the basics of machine learning.
• Get an overview of Deep Learning and explore today’s most advanced
facial recognition methods using Deep Learning.
• Building training data set including images for face detection, images
for face recognition.
• Building model evaluation data set including images for face detection
and images for face recognition. For the evaluation of the respective
models in each stage.
• Building a celebrity face recognition website application in Vietnam in
images.
Chapter 1 INTRODUCTION
1.1
Problem statements
Nowadays, with the development of information technology, data is distributed everywhere, every second million images and videos are uploaded to the Internet and
distributed quickly. In particular, social networks are where people regularly post
pictures. The meaning of posting images is often to share memories, experiences,
products, and personal art with others. The images of celebrities often have large
shares and interactions on social networks. This is an advantage of celebrities in the
field of product advertising.
Currently, many domestic and foreign companies have been solving face recognition problems such as Apple using face recognition to unlock mobile devices; Facebook uses a friend face tagging system to connect with the community; Financial
companies have used face recognition to authenticate payments instead of hard cards;
airports and terminals use face recognition to control security; Schools, companies
want to use automatic attendance and attendance systems through facial authentication, ..., but the recognition of faces of famous people in Vietnam in images is very
few people mind and especially with celebrities in Vietnam, while more and more
pictures are posted on social networks.
Based on our limited understanding during the survey period, we find that there
have been many studies on the face recognition problem, many models were given,
many pre-trained models were public with free public face datasets. The results
achieved on this data set are very good, but many observations show that applying
them to real problems in Vietnam is not as good. With the aim of building research
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(a) My Tam
(b) Chi Pu
(c) Son Tung MTP
(d) Tran Thanh
Figure 1.1: Some celebrity people in Vietnam.
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purposes, capturing technology, optimizing the human face recognition problem for
Vietnamese, we decided to research and build a celebrity face recognition system in
Vietnam. This and that is also the reason why the Vietnamese celebrity identity system in the image came into being. With this system, we can extract information in the
image so that it can be applied in many different fields, especially in the recommendation system.
In recent years, the amount of data appears more and more, this contributes to the
rapid development of Deep Learning and gradually becomes a trend with improved
computing speed. Deep Learning methods and algorithms achieve better results than
the handcrafted approach. That is why the Deep Learning approach is interested in the
research community. Therefore, in the process of researching and developing Deep
Learning, the number of algorithms and methods from there appeared more and more
rich and diverse. And to know which algorithm is suitable for use in this identification
problem, we have conducted a survey and evaluation on many methods to choose the
most suitable method for use. From then apply this method to build up the identifier
used to develop applications in the future.
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1.2
Goals and scope
1.2.1
Goals
To solve the problem of face recognition in images. We have set out particular goals
to get the thesis done:
• Get an overview of machine learning, the basics of machine learning.
• Get an overview of Deep Learning and explore today’s most advanced facial
recognition methods using Deep Learning.
• Building training data set including images for face detection, images for face
recognition.
• Building model evaluation data set including images for face detection and images for face recognition. For the evaluation of the respective models in each
stage.
• Building a celebrity face recognition website application in Vietnam in images.
1.2.2
Scope
The scope of our thesis consists:
• Face recognition celebrity people in Vietnam.
• Execute algorithm evaluation of each stage: face detection, face recognition
• Build a list of celebrity people in Vietnam and a data set of celebrity people in
Vietnam for the face recognition phase, data is collected on Google Image
• Building illustration applications for the problem of face recognition celebrity
people in Vietnam in images.
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1.3
Contributions
These are some of the contributions that we made after making the thesis:
• Researching knowledge and approaches in accordance with the face recognition
approaches, especially approaches based on Deep Learning.
• We build a dataset exclusively for research purposes, to optimize the problem of
human facial recognition for Vietnamese
• We evaluate state-of-the-art face detection approaches based on different aspects
including the execution time, accuracy, resource usage, along with the trade-off
among avariety of different inputs and its base network. Following the results
we achieved, we make our analyses how to choose a suit-able models for face
detecting.
• Building illustrations that users use to recognition celebrity people in images.
1.4
Outline
Chapter 1: General introduction.
Chapter 2: Related works.
Chapter 3: Face recognition with deep learning.
Chapter 4: Experiment.
Chapter 5: Demonstration.
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