Submission deadlines: 5:00 pm, Friday 16th September, 2022

Value: 15% of CITS1401.

To be completed individually.

You should construct a Python 3 program containing your solution to the following problem and submit your program electronically on Moodle. The name of the file containing your code should be your student ID e.g. 12345678.py. No other method of submission is allowed. Your program will be automatically run on Moodle for sample test cases provided in the project sheet if you click the “check” link. However, your submission will be tested thoroughly for grading purposes after the due date. Remember you need to submit the program as a single file and copy-paste the same program in the provided text box. You have only one attempt to submit so don’t submit if you are not satisfied with your attempt. All open submissions at the time of the deadline will be automatically submitted. There is no way in the system to open the closed submission and reverse your submission.

You  are  expected to  have  read  and  understood the  University's guidelines on academicconduct.  In accordance  with this  policy,  you  may  discuss  with  other  students the  general  principles  required to understand this  project,  but the work you  submit  must  be the  result of  your own effort.  Plagiarism detection, and other systems for detecting potential malpractice, will therefore be used. Besides, if what you submit is not your own work then you will have learnt little and will therefore, likely, fail the final exam.

You must submit your project before the deadline listed above. Following UWA policy, a late penalty of 5% will be deducted for each day (or part day), after the deadline, that the assignment is submitted. No submissions will be allowed after 7 days following the deadline except approved special consideration

cases.

Overview

Face  recognition (FR) is one the  most widely  known and  used non-intrusive  biometric which  helps in identifying people. Given the identity of a known person, the goal of an FR system is to recognize the same person in a different scenario, for example while displaying a different expression. You can readthispaper to understand more about FR under different expressions.

Your task is to help the researchers in analysing eight geodesic (surface) and eight 3D Euclidian distances between  a  few  facial  landmarks  across  four  expressions 'Neutral', 'Angry', 'Disgust', 'Happy'.  These distances on one face (in Neutral expression) can then be used to calculate similarity with the same face in different expressions or with other faces in the data set to see which faces are closer to (or look like) the reference face. Table 1 provides the details of each landmark while Figure 1 shows their location on the face. Table-2 gives you the details of the distances which will be used.

The attached csv (comma separated values) file has the eight geodesic and 3D Euclidian distances for the four expressions mentioned above for each person. For the sake of simplicity, the Distance numbers are given instead of their names. For example, Inner-canthal width is distance number 1 and Lower jaw width is distance number 8. Your task is towrite a program which fulfills the following requirements.

Table 1: Details of the Facial landmarks.

Figure 1: Facial landmarks locations on face

Table 2: List  of  distances  used  in  this project. For example, Forehead width is the distance between Left Forehead (FT_L) and Right   Forehead   (FT_R).   Similarly   Inner canthal width, is the distance between the

two inner eye corners: En_L and En_R.

Requirements (i.e. what your program should do)

Input:

Your program must define the function main with the following syntax:

def main(csvfile, adultID, Option):

The input arguments to this function are:

•   csvfile: The name of the CSV file containing the data of individuals which needs to be analysed. Below are the first two rows of a sample file.

The first row of the CSV file contains the headers “Adult ID”, “Expression”, “Distance”, “GDis” and

“LDis” . From the second row, the first value of each row contains the de-identified adult ID, the

expression displayed by the face, distance number (refer to Table-2), the geodesic distance and

the 3D  Euclidean distance . We do not have prior knowledge about the number of subjects we

have to analyse (i.e. the number of rows) that the CSV file contains. Adult ID and Expression are strings, Distance is an integer while the remaining values are floats.

•   adultID: The ID number of the adult we have to analyse. Remember that the ID is a string and is case sensitive.

•   Option: The input argument that decides the type of analysis required. It can take only one of the two string inputs: “stats”or “FR”. If the third input argument is “stats”, then the objective of the program is to carry out some statistical analysis of the adult whose ID is given in the second argument. Otherwise, if the third input argument is “FR” then the objective is to perform face recognition by calculating the cosine similarity between the reference ID and other subjects.

Output:

The function is required to return the following outputs in the order provided below:

When the third input argument is “stats” then for the adult given in the input argument “adult ID”

1. OP1- A list of lists containing the minimum (non-zero)  and maximum GDis and LDis of each distance across the four expressions. For example, the minimum (non-zero) intercanthal width (geodesic and 3D Euclidean) in Neutral, Angry, Disgust and Happy expressions. There will be 8 lists inside the main list and each list will have four elements in the following order : [minimum GDis, maximum GDis, minimum LDis, maximum LDis]. The distances must be in the same order as given in Table-2.

2. OP2- A list of lists containing the difference between the geodesic and 3D Euclidean distances for each expression. There will be 4 lists inside the main list and each list will have eight elements.

3. OP3- A list containing the average geodesic distance of the eight distances across the four expressions. This list will have 8 elements.

4. OP4- A list containing the standard deviation of the 3D Euclidean distance of the eight distances across the four expressions. This  list will  have 8 elements. The formula to calculate standard deviation is provided at the end of this project sheet.

When the third input argument is “FR”:

You will calculate the cosine similarity between the geodesic distances of the neutral expression of reference adult ID and the geodesic distances of the remaining expressions of the same ID as well as the neutral expressions of the remaining subjects in the dataset. Your output will be:

1.   The ID of the person with which the reference face has the maximum cosine similarity, and

2.   The maximum cosine similarity value. The formula to calculate cosine similarity is provided at the end of this project sheet.

All returned lists should have the values in order. For example, for OP2, the main list has 4 sub-lists which should be ordered according to the expressions 'Neutral', 'Angry', 'Disgust', 'Happy'. Inside each sub-list are 8 elements for the distances which must be ordered as per Table-2. All returned numeric outputs (both in lists and individual) must contain values rounded to four decimal places (if required to be rounded off). Do not round the values during calculations. Instead round them only at the time when you save them into the final output variables.

Examples:

Download the ExpData_Sample.csv file from the folder of Project 1 on LMS or Moodle. Some examples of how you can call your program from the Python shell (and examine the results it returns) are:

>>> OP1,OP2,OP3,OP4 = main('ExpData_Sample.csv','E001','stats')

>>> OP1

[[45.0087, 48.448, 29.9972, 32.9404], [104.1724, 110.913, 93.9636, 98.5776], [33.9933, 39.7572, 34.695, 40.0872], [49.5445, 66.477,

31.0483, 34.1107], [5.5607, 9.7036, 5.4345, 9.3448], [7.4147, 8.9331, 7.4147, 8.7645], [150.0258, 175.289, 118.1052, 127.0695], [137.8113, 154.4196, 107.0492, 111.6436]]

>>> OP2

[ [16.3175, 10.2088, -0.5075, 34.6928, 0.3588, 0.0187, 48.2195, 46.5544], [15.2932, 12.3354, -0.33, 18.4962, 0.1669, 0.1686, 39.2716, 45.8233],   [13.1529, 9.3969, -0.7016, 27.0089, 0.3791, 0.0, 39.5195, 36.9674],     [15.4137, 10.9684, -0.6347, 24.5052, 0.1262, 0.2101, 31.9206, 26.9959]]

>>> OP3

[46.7753, 106.8638, 37.9698, 58.3879, 7.5469, 8.1489,160.8882, 148.6113]

>>> OP4

[1.0775, 1.6565, 2.2229, 1.1442, 1.408, 0.6276, 3.5032, 1.8129] >>> ID,cossim = main('ExpData_Sample.csv','E001','FR')

>>> ID

'A004'

>>> cossim

0.9992

Assumptions:

Your program can assume the following:

•    Anything that is meant to be a string (i.e. header row) will be a string, and anything that is meant to be a number (i.e. data) will be a number.

•    The order of columns in each row will follow the order of the headings provided in the first row.

•    The data of each individual adult will always be contiguous. It will never be spread out randomly.

• The data for each expression will always be contiguous and in the order : 'Neutral', 'Angry', 'Disgust',

'Happy'.

•   The data for distance may or may not be in order as in Table-2. For example, it is possible that the 6th

distance of a particular expression is listed first and the 1st distance is listed last.

•    Distances can never be negative or zero. If any distance is negative or equal to zero, then

replace it with 50.

•    No data will be missing in the csv file.

•    The main() function will always be provided with valid input parameters.

•    The formula for standard deviation and for calculating cosine similarity can be found at the end of the project sheet.

Important grading instruction:

Note that you have not been asked to write specific functions. This task has been left to you.  However, it is essential   that   your   program   defines   the   top-level    function main(csvfile, adult, Option) (hereafter referred to as “main()” to save space when writing it. Note that when “main()” is written it still implies that it is defined with its three input arguments). The idea is that within main(), the program calls the other functions. (Of course, these functions may then call further functions.) This is important because when your code is tested on Moodle, the testing program will call your main() function. So, if you fail to define main(),

the testing program will not be able to test your code and your submission will be graded zero. Don’t forget

the submission guidelines provided at the start of the project sheet.

Things to avoid:

There are a few things for your program to avoid.

• You are not allowed to import any Python module. While use of many of these modules, e.g. csv or math is a perfectly sensible thing to do in production setting, it takes away much of the point of different aspects of the project, which is about getting practice opening text files, processing text file data, and use of basic Python structures, in this case lists and loops.

•   Do not assume that the input file names will end in .csv. File name suffixes such as .csv and .txt are not

mandatory in systems other than Microsoft Windows. Do not enforce that within your program that the file must end with a .csv or any other extension (or try to add an extension onto the provided csvfile

argument), doing so can easily lead to loosing marks.

•   Ensure your program does NOT call the input() function at any time. Calling the input() function will cause your program to hang, waiting for input that automated testing system will not provide (in fact, what will happen is that if the marking program detects the call(s), it will not test your code at all which may result in zero grade).

•   Your program should also not call the print() function at any time. If it has encountered an error state and is exiting gracefully then your program needs to return zero as values for the cosine similarity otherwise empty lists or empty string. At no point should you print the program’s outputs instead of (or in addition to) returning them or provide a printout of the  program’s progress in calculating such outputs.

Disclaimer: Although  this  project  addresses  a  real-world  problem,  the  files  provided  to  you  contain synthetically generated data.

Submission:

Submit your solution on Moodle before the deadline as per details provided at the start of the project sheet.

You need to contact unit coordinator if you have special considerations or you plan to be making a submission after the mentioned due date.

Marking Rubric:

Your program will be marked out of 30 (later scaled to be out of 15% of the final mark).

22 out of 30 marks will be awarded automatically based on how well your program completes a number of tests, reflecting normal use of the program, and also how the program handles various states including, but not limited to, different numbers of rows in the input file and / or any error states. You  need to think creatively what your  program  may face. Your submission will be graded by data files other than the provided data file. Therefore, you need to be creative to look into corner or worst cases. I have provided few guidelines from ACS Accreditation manual at the end of the project sheet which will help you to understand the expectations.

8 out of 30 marks will be awarded on style (5/8) “the code is clear to read” and efficiency (3/8) “your program is well constructed and runs efficiently” . For style, think about use of comments, sensible variable names, your name and student ID at the top of the program, etc. (Please watch the lectures where this is discussed).