Facial expression detection using Deepface module in Python

Facial expression detection is a popular application of computer vision and deep learning. Deepface is a Python module for facial recognition and facial expression analysis. It is built on top of Keras and TensorFlow and provides a simple API for detecting facial expressions in images and videos.

Here are the steps to perform facial expression detection using Deepface module in Python:

• Install Deepface module: You can install Deepface module using pip command in your terminal or command prompt.

pip install deepface

• Import necessary libraries: Import the necessary libraries such as Deepface, OpenCV, and Matplotlib.

import cv2
import matplotlib.pyplot as plt
from deepface import DeepFace

• Load the image: Load the image on which you want to perform facial expression detection using OpenCV.

img = cv2.imread('image.jpg')

• Detect faces: Use the detectFace function of DeepFace module to detect faces in the image.

faces = DeepFace.detectFace(img, detector_backend='opencv')

• Analyze facial expressions: Use the analyze function of DeepFace module to analyze the facial expressions of the detected faces.

result = DeepFace.analyze(faces, actions=['emotion'])

• Visualize the results: Use Matplotlib to visualize the results.

plt.imshow(cv2.cvtColor(faces[0], cv2.COLOR_BGR2RGB))
plt.title('Detected Face')
plt.show()

print('Facial Expressions:')
for emotion, score in result['emotion'].items():
    print(f'{emotion}: {score}')

This will display the detected face and the facial expressions detected in the image.

There are other methods to perform facial expression detection using Deepface module such as using a pre-trained model, using a webcam to detect facial expressions in real-time, and using a video file to detect facial expressions. Here are some examples:

• Using a pre-trained model:

model = DeepFace.build_model('FacialExpression')
result = DeepFace.analyze(img, model=model, actions=['emotion'])

• Using a webcam:

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    faces = DeepFace.detectFace(frame, detector_backend='opencv')
    result = DeepFace.analyze(faces, actions=['emotion'])

    for face in faces:
        cv2.imshow('Facial Expression Detection', face)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

• Using a video file:

cap = cv2.VideoCapture('video.mp4')

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    faces = DeepFace.detectFace(frame, detector_backend='opencv')
    result = DeepFace.analyze(faces, actions=['emotion'])

    for face in faces:
        cv2.imshow('Facial Expression Detection', face)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

These are some of the methods to perform facial expression detection using Deepface module in Python.