EMOTION DETECTION USING AI
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The document discusses the concept of emotion detection using AI, focusing on facial expression recognition, its challenges, and implementation methods. It explores the technology's applications in various fields such as video games, automotive safety, and healthcare, while outlining the necessary tools like OpenCV and Keras for development. Additionally, it emphasizes the future possibilities of enhancing market research and supporting individuals with autism through this technology.
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from keras.models import load_model
from time import sleep
from keras.preprocessing.image import img_to_array
from keras.preprocessing import image
import cv2
import numpy as np
face_classifier =
cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
classifier =load_model('./Emotion_Detection.h5')
class_labels = ['Angry','Happy','Neutral',]
cap = cv2.VideoCapture(0)
while True:
# Grab a single frame of video
ret, frame = cap.read()
labels = []
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray,1.3,5)
for (x,y,w,h) in faces:
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h,x:x+w]
roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)
if np.sum([roi_gray])!=0:
roi = roi_gray.astype('float')/255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi,axis=0)
# make a prediction on the ROI, then lookup the class
preds = classifier.predict(roi)[0]
print("nprediction = ",preds)
label=class_labels[preds.argmax()]
print("nprediction max = ",preds.argmax())
print("nlabel = ",label)
label_position = (x,y)
cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,
(0,255,0),3)
else:
cv2.putText(frame,'No Face Found',
(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)
print("nn")
cv2.imshow('Emotion Detector',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release() cv2.destroyAllWindows()](https://image.slidesharecdn.com/miniprojectpresentation1-210617135622/85/EMOTION-DETECTION-USING-AI-9-320.jpg)
EMOTION DETECTION USING AI
- 1. Emotion Detection Using AI How Emotion Detection can change the future BY-ARYAN TRISAL
- 2. In this Presentation Here's what we have covered: Introduction Problems Faced Solutions Need for the proposed system Implementation Code Future Scope
- 3. Introduction Facial expression is an essential part of communication. For this reason, the issue of human emotions evaluation using a computer is a very interesting topic, which has gained more and more attention in recent years. It is mainly related to the possibility of applying facial expression recognition in many fields such as HCI, video games, virtual reality, and analysing customer satisfaction etc. Emotion’s determination (recognition process) is often performed in 3 basic phases: face detection, facial features extraction, and last stage - expression classification.
- 4. Facial emotion recognition is a complex task and the machine learning approach to recognize faces requires several steps to perform it, some are: Feature selection: This stage refers to attribute selection for the training of the machine learning algorithm. The process includes the selection of predictors for construction of the learning system Feature classification: When it comes to supervised learning algorithms, classification consists of two stages. Training and classification, where training helps in discovering which features are helpful in classification. Feature extraction:Machine learning requires numerical data for learning and training. During feature extraction, processing is done to transform arbitrary data, text or images, to gather the numerical data Classifiers:This is the final step in this process. Based on the inference from the features, the algorithm performs data classification.
- 5. Problems Faced Technical challenges Emotion recognition shares a lot of challenges with detecting moving objects in the video identifying an object, continuous detection, incomplete or unpredictable actions, etc. Data augmentation As with any machine learning and deep learning algorithms, ER solutions require a lot of training data. This data must include videos at various frame rates, from various angles, with various backgrounds, with people of different genders, nationalities, and races, etc.
- 6. Solutions 1.Create your own dataset: This is the most expensive and time-consuming way, but you’ll end up with a dataset perfectly suited for your task. 2.Combine several datasets: You can cross-check the performance of your solution on several other datasets. 3.Modify the data as you go: Some researchers suggest editing videos that you’ve already used: crop them, change the lighting, slow them down, speed them up, add noise, etc.
- 7. Need for the proposed system Detecting emotions with technology is quite a challenging task, yet one where machine learning algorithms have shown great promise. By using Facial Emotion Detection, businesses can process images, and videos in real-time for monitoring video feeds or automating video analytics, thus saving costs and making life better for their users. Some examples are given below: Emotion recognition in video game testing Automotive industry and emotion recognition Emotion recognition in Health Care
- 8. Implementation Tools and Libraries used OpenCV OpenCV is the library we will be using for image transformation functions such as converting the image to grayscale Deep Learning Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabelled NumPy NumPy is a library for the Python programming language, adding support for large, multi- dimensional arrays Keras Keras is an open-source software library that provides a Python interface for artificial neural networks. Python Python is a powerful scripting language and is very useful for solving statistical problems involving machine learning algorithms
- 9. CODE from keras.models import load_model from time import sleep from keras.preprocessing.image import img_to_array from keras.preprocessing import image import cv2 import numpy as np face_classifier = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml') classifier =load_model('./Emotion_Detection.h5') class_labels = ['Angry','Happy','Neutral',] cap = cv2.VideoCapture(0) while True: # Grab a single frame of video ret, frame = cap.read() labels = [] gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) faces = face_classifier.detectMultiScale(gray,1.3,5) for (x,y,w,h) in faces: cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2) roi_gray = gray[y:y+h,x:x+w] roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA) if np.sum([roi_gray])!=0: roi = roi_gray.astype('float')/255.0 roi = img_to_array(roi) roi = np.expand_dims(roi,axis=0) # make a prediction on the ROI, then lookup the class preds = classifier.predict(roi)[0] print("nprediction = ",preds) label=class_labels[preds.argmax()] print("nprediction max = ",preds.argmax()) print("nlabel = ",label) label_position = (x,y) cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2, (0,255,0),3) else: cv2.putText(frame,'No Face Found', (20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3) print("nn") cv2.imshow('Emotion Detector',frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()
- 10. FUTURE SCOPE Automotive Safety and Research Systems: For the safety of the driver and passengers by recognizing the facial expression of the driver. Medical Research into Autism: Ability to read the facial expression of people with autism and finding the best solutions for them. Market Research: Facial expression marketing to help get the necessary information regarding respective trends. Pay-per-Laugh: Faced with rising ticket prices and declining audiences in 2014, a Spanish theatre experimented with charging audiences 30 cents a laugh for its comedy shows, installing emotion recognition cameras in front of each seat, and fixing a ceiling of 24 euros per customer for the more successful events.