README.md

Facial-Recognition-and-Emotion-Recognition-project

Development of question generation and mock interview function based on model learning and self-introduction to recognize and classify facial emotions

Team Members

• 김범찬
• 방서빈
• 송승민 - Leader
• 한상준

Development environment settings

Device - GPU : NVIDIA A100 80GB (x 4)
CUDA Version : 12.2
Storage : 2.0T

• requirements.txt

$ pip install -r requirements.txt

• mathai.yaml

$ conda env create -f mathai.yaml
$ conda activate mathai

Modularization of each model

• config : Hyperparameter values required for each file
• dataset : It's made into a transformed image folder format dataset
• model : Each classification model
• train : Train for each model
• plot : Plot the accuracy, loss, and confusion matrix of the model
• main : where implementation is carried out comprehensively

Origin Data tree

📦data
 ┣ 📂anger
 ┃ ┣ 📂labeled
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┃ ┗ 📂raw
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┣ 📂anxiety
 ┃ ┣ 📂labeled
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┃ ┗ 📂raw
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┣ 📂embarrass
 ┃ ┣ 📂labeled
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┃ ┗ 📂raw
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┣ 📂happy
 ┃ ┣ 📂labeled
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┃ ┗ 📂raw
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┣ 📂normal
 ┃ ┣ 📂labeled
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┃ ┗ 📂raw
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┣ 📂pain
 ┃ ┣ 📂labeled
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┃ ┗ 📂raw
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┗ 📂sad
 ┃ ┣ 📂labeled
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation
 ┃ ┗ 📂raw
 ┃ ┃ ┣ 📂train
 ┃ ┃ ┗ 📂validation

Preprocessing

1. Truncated File Detection

***truncated image file sample***

• train : embarrass(#1), normal(#3)
• validation : anger(#2), embarrass(#1), happy(#4), pain(#1)
• Excluding a total of 12 image files

2. Split Dataset

	non_crop	non_crop	non_crop	crop	crop
train	1400	7000	28000	1400	7000
validation	700	2100	7000	700	2100

3. Create data path and labels for Yolov8

📦data
┣ 📂train
┃  ┣ 📂images
┃  ┗ 📂labels
┣ 📂validation 
┃  ┣ 📂images
┃  ┗ 📂labels
┗ 📂test
   ┗ 📂images

Part1 Categorizing a person's feelings

Used model

VGG

$ ~/model/vgg/$ python main.py

ResNet

$ ~/model/vgg/$ python main.py 

ResNeXt

$ ~/model/vgg/$ python main.py 

ViT

$ ~/model/vgg/$ python main.py 

Yolov8 - cls

from ultralytics import YOLO

model = YOLO('yolov8n-cls.pt')  # load a pretrained YOLOv8n cldssification model
model.train(data='image_folder_path')

Yolov8 - detection

from ultralytics import YOLO

model = YOLO('yolov8m.pt')  # load a pretrained YOLOv8n cldssification model
model.train(data='yaml_file_path')

Classification result

What is the best model?

• Yolov8-cls

Model	loss	acc
VGG	1.4069	0.4864
ResNet	1.6695	0.3563
ResNeXt	1.5422	0.4163
ViT	1.7797	0.3006
Yolov8-cls	0.8322	0.6360

Part2 나만의 One and Only 면접 코디 (with. 감정분류)

Simulation of interviews using AI

• Cold job market
• Alone person
• Without time and place constraints
• Create Customized Questions

UI

• Streamlit

Facial Detection

Yolov8 - detection

• Live analysis of interviews

emotion	anger	anxiety	embarrass	happy	normal	pain	sad
Type	Negative	Negative	Negative	Positive	Positive	Negative	Negative

How did you score your emotions?

def emotion_score(score:50.0,emotion):
  '''
  Functions that give scores for each emotion

  agrs - score : float=50.0
       - emotion : list
  '''
  score = score
  good, bad = 0, 0
  threshold = 0.1 # initial threshold

  good += (threshold * (emotion.count('happy')) + threshold * (emotion.count('normal')))
  bad -=  (threshold * (emotion.count('anger')) + threshold * (emotion.count('embarrass')) + threshold * (emotion.count('anxiety')) + threshold * (emotion.count('pain')) + threshold * (emotion.count('sad')))
  print(emotion)

  # initial score range
  if score+good+bad > 70:
      print('good')
  elif score+good+bad > 40:
      print('normal')
  else:
      print('bad')

  print(score+good+bad)
  return score+good+bad

Live Emotion Detection

Create Question

GPT-3.5

• Pretrained model
  

Prompts that match our needs

SYSTEM_ANSWER_PROMPT = """You are an expert on generating 3 interview questions in Korean based on the provided 자기소개서 (self-introduction), a helpful bot who provides polished and professional questions which are commonly asked interview questions.
Your task is to understand the self-introduction provided by the user and then enhance the interview questions.
Also don't use heavy or complex words that are not typically used in human conversations.
"""

Self-Introduction based questions

Speech to Text & Evaluation Answer

GPT-3.5

OpenAI-Whisper

Answers to Questions

Utilization

You can combine part2's Simulation of interviews with ESTsoft's AI Human

sources ESTsoft

Creating AI Interviewers Using AI Human

Interviewer : Create questions based on letter of self-introduction

sources Freepik

Candidate : Practice listening to questions and telling answers

Result

Model	accuracy	precision	recall	f1_score	time(h)
VGG	0.4864	0.420	0.414	0.417	26
ResNet	0.3563	0.28	0.298	0.289	27.2
ResNeXt	0.4163	0.202	0.201	0.201	18.2
ViT	0.3062	0.307	0.308	0.307	14.6
Yolov8m-cls	0.69	0.63	0.62	0.625	approx 3
Yolov8m	0.5725	0.68	0.74	0.709	approx 3

Reference

Whisper
Yolov8