A.5 Dataset Used in Caching Methodologies Facial Expression Images
4. SELF EVALUATION OF THE PROGRESS
• Require more exploration on the mobile programming to improve the speed of development
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-27 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 3 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Application of OpenCV face detection on both the camera frames and stored images
• Application of image processing techniques provided by the OpenCV
2. WORK TO BE DONE
• Try to call the AWS rekognition for recognizing the facial expression from the camera frame and the images from the gallery.
3. PROBLEMS ENCOUNTERED
• Following the guidance of performing facial expression recognition with a self-trained model, a number of functions, such as AscynTask and
StartActivityForResult, have been deprecated. It took some time for me to adjust to the new way of performing the same steps..
4. SELF EVALUATION OF THE PROGRESS
• Acceptable even now
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-28 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 4 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Successful calling the AWS rekognition for recognizing the facial expression from the stored images.
2. WORK TO BE DONE
• Starting the expressional change detection exploration by getting advice from the supervisor.
3. PROBLEMS ENCOUNTERED
• When I attempted to invoke AWS rekognition from the mobile app, I discovered that the accessible android SDK had not been updated in a long time and that the redirecting link was no longer available. It took me a considerable amount of time reading the AWS recommendations in order to build the s3 bucket and AWS amplify. However, I discovered an article from 2019 outlining the procedures necessary to integrate the AWS SDK into mobile programming. I was able to use the image stored in the S3 bucket for face expression recognition by following the guide. I was initially unaware that the same code that invokes the AWS recognition service could also send the image as a byte buffer.
4. SELF EVALUATION OF THE PROGRESS
• I have speed too much on understanding the implementation logic of AWS service on mobile development, but nothing useful is obtained. At the moment I read the AWS documentation, there are a lot of redirecting links not working and the documentation is not exactly the same when I really try them out, some interfaces and the functions are not the same.
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-29 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 5 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Calculate the required bandwidth when sending the AWS request
• Dataset gathering
2. WORK TO BE DONE
• Requires precise tracking of the time it takes to receive a response from AWS, the time it takes to execute the image processing approach, and the bandwidth consumed prior to and after image processing.
3. PROBLEMS ENCOUNTERED
• Not a huge issue, however, the initial directions for completing the project were incorrect. The supervisor reminded me that the project's focus is on performance and battery usage and that I must demonstrate that the recommended methods increase AWS processing speed while simultaneously reducing necessary bandwidth.
4. SELF EVALUATION OF THE PROGRESS
• My focus was diverted by other course assignments, which resulted in a minor delay in progress. I should make a greater effort with time
management.
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-30 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 6 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Done the algorithm for the loop until getting the average time elapsed for image processing and AWS processing time and the average bandwidth used in sending the AWS request.
• Done dataset cleaning and labeling.
2. WORK TO BE DONE
• Perform the evaluation using the loops and records the results
• Visualize the results
3. PROBLEMS ENCOUNTERED
• To obtain the average metrics with minimum changes requires the computation of loss. It is confusing if using the difference between the average of accumulated values between the current and previous loop will be suitable. However, Sir has confirmed my algorithm.
4. SELF EVALUATION OF THE PROGRESS
• Dataset labeling took me some time, and I should speed up on the visualization parts.
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-31 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 7 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Done measuring the time elapsed for the processing time of converting image to grayscale, detecting face and cropping images
• Done measure the bandwidth required and time elapsed to call AWS rekognition using different types of images
2. WORK TO BE DONE
• Select a new set of data to get 100% accuracy from the AWS rekognition using the original images
3. PROBLEMS ENCOUNTERED
• The time elapsed for the AWS rekognition to respond using the original images and the face cropped grayscale images have no big differences and the time reduced is less than 0.1 seconds. However, the face detection using OpenCV required about 0.1 seconds. Implementing all the preprocessing steps might not really reduce the time elapsed to get the AWS response.
• While the dataset used is having only 50% of accuracy when using the original images, therefore supervisor had advised me to get another set of images that possibly get 100% accuracy without any image processing implemented.
4. SELF EVALUATION OF THE PROGRESS
• Still acceptable but probably should start writing the report
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-32 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 8 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Re-evaluation of time elapsed, bandwidth used, and accuracy when calling the AWS Rekognition using images with different image processing techniques applied.
• Rework chapter 3 and drafted chapters 4 and 5 in the report
2. WORK TO BE DONE
• Finishing chapters 4 and 5
• Start developing the teacher and student CNN model by training a basic model
3. PROBLEMS ENCOUNTERED
• The teacher and student CNN model is a new thing to me and I still a bit blur on this.
4. SELF EVALUATION OF THE PROGRESS
• I should work harder to complete the report first and try to get the teacher and student model done.
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-33 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 9 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Chapters 4 and 5 are done but to be revised later
• The basic CNN model was built
2. WORK TO BE DONE
• Applying knowledge distillation on the CNN model to achieve teacher- student machine learning model
• Exploring caching and ImageMagick for facial expression comparison
3. PROBLEMS ENCOUNTERED
• No problems
4. SELF EVALUATION OF THE PROGRESS
• Should put more effort on report writing
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-34 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 10 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Done the image comparison using ImageMagick diff
• Done the Model training using the teacher-student learning model
• Done rework chapter 3
2. WORK TO BE DONE
• Get another FER dataset for the same person to be used in image comparison and training the model again
• Complete chapters 4, 5, and 6
3. PROBLEMS ENCOUNTERED
• No
4. SELF EVALUATION OF THE PROGRESS
• Need to rush up for the report
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-35 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 11 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Retrain the FER teacher-student machine learning model with ResNet50 as the foundation
• Organize the android application coding
• Organize the codes of expression comparison with cached image and teacher-student machine learning model
• Completed chapter 3, 4 and 5
2. WORK TO BE DONE
• Revise chapter 1
• Completing chapter 6
• Formatting the FYP report
3. PROBLEMS ENCOUNTERED
• The search algorithm becomes the bottleneck when comparing the new expression with cached expressions. It took up approximately 11 minutes to complete compared with all 133 expressions.
4. SELF EVALUATION OF THE PROGRESS
• The progress is still manageable
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-36 FINAL YEAR PROJECT WEEKLY REPORT
(Project II)
Trimester, Year: Trimester 3 Year 3 Study week no.: Week 12 Student Name & ID: Tan Wei Mun, 18ACB03705
Supervisor: Ts Dr. Ooi Boon Yaik
Project Title: Facial Expression Recognition
1. WORK DONE
• Completed chapter 6
• Modified chapter 1
• Formatted report
2. WORK TO BE DONE
• Add appendix
• Turnitin report
• Make a new poster
• Prepare presentation
3. PROBLEMS ENCOUNTERED
• no
4. SELF EVALUATION OF THE PROGRESS
• Need to ready for the presentation
_________________________ _________________________
Supervisor’s signature Student’s signature
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
A-37 A.8 Poster
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
PLAGIARISM CHECK RESULT
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR
FACULTY OF INFORMATION AND COMMUNICATION
TECHNOLOGY Full Name(s) of
Candidate(s) Tan Wei Mun
ID Number(s) 18ACB03705
Programme / Course Computer Science
Title of Final Year Project Live Facial Expression Recognition
Similarity
Supervisor’s Comments(Compulsory if parameters of originality exceeds the limits approved by UTAR) Overall similarity index: __7_ %
Similarity by source
Internet Sources: 4 % Publications: 3 % Student Papers: 4 %
Number of individual sources listed of more than 3% similarity: 0
Parameters of originality required and limits approved by UTAR are as Follows:
(i) Overall similarity index is 20% and below, and
(ii) Matching of individual sources listed must be less than 3% each, and (iii) Matching texts in continuous block must not exceed 8 words
Note: Parameters (i) – (ii) shall exclude quotes, bibliography and text matches which are less than 8 words.
Note Supervisor/Candidate(s) is/are required to provide softcopy of full set of the originality report to Faculty/Institute
Based on the above results, I hereby declare that I am satisfied with the originality of the Final Year Project Report submitted by my student(s) as named above.
______________________________
Signature of Supervisor
Name: Ts Dr. Ooi Boon Yaik Date: ___________________________
Universiti Tunku Abdul Rahman
Form Title : Supervisor’s Comments on Originality Report Generated by Turnitin for Submission of Final Year Project Report (for Undergraduate Programmes) Form Number: FM-IAD-005 Rev No.: 0 Effective Date:
01/10/2013
Page No.: 1of 1
21/4/2022
Bachelor of Computer Science (Honours)
Faculty of Information and Communication Technology (Kampar Campus), UTAR