Đồ án tốt nghiệp - Facial landmark detection on multiple platforms using quantized mobilenetv2 FEAA

MINISTRY OF EDUCATION AND TRAINING
HCMC UNIVERSITY OF TECHNOLOGY AND EDUCATION
--

GRADUATION THESIS
MAJOR: COMPUTER ENGINEERING TECHNOLOGY

FACIAL LANDMARK DETECTION ON MULTIPLE
PLATFORMS USING QUANTIZED MOBILENETV2
FOR EDGE AI APPLICATIONS

INSTRUCTOR

PHAM VAN KHOA

STUDENT

DO MANH DUNG
DANG TRUNG NGHIA

HO CHI MINH CITY – 07/2025

DISCLAIMER
The implementation group hereby formally s that the research and application
presented in this thesis represent the outcome of our independent and original work. The
methodologies, analytical techniques, and proposed solutions have been developed based
on our professional expertise and scientific research capabilities. All aspects of the research
process, including data collection, preprocessing, model design, training, and system
deployment, have been conducted with scientific rigor and methodological precision.
We categorically affirm that no portion of this work has been plagiarized from any
published materials without appropriate citation and acknowledgment. All reference
materials have been properly documented and credited according to established academic
standards. We have maintained strict adherence to ethical principles and academic integrity
throughout the entire research and implementation process.
We acknowledge full responsibility for the accuracy and authenticity of all content
presented in this thesis. Any errors or omissions that may have occurred during the
implementation process are entirely our responsibility, and we commit to upholding the
highest standards of academic and research integrity.
The research findings and results presented in this thesis demonstrate our systematic
approach and innovative methodology in the field of license plate recognition. We
anticipate that this research will make a meaningful contribution to the advancement of the
field and provide a foundation for subsequent research endeavors.
The knowledge and experience gained through this project have been invaluable to our
professional development. We are confident that the technical solutions and research
outcomes presented herein reflect our dedicated commitment to scientific excellence and
innovation in computer vision and pattern recognition applications.
Students
Do Manh Dung
Dang Trung Nghia

i

ACKNOWLEDGEMENT
We would like to express our deepest gratitude to Ph.D. Phạm Văn Khoa, our esteemed
supervisor and mentor, for his invaluable guidance and unwavering support thro
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