Python Tutorial: Face Recognition with Deepface Models

Facial emotion recognition using Python has gained significant traction in various fields, from market research to healthcare, thanks to its ability to analyze human emotions through facial expressions. Among the array of tools available for this task, DeepFace stands out as a powerful Python library that streamlines the process with its pre-trained deep learning models tailored specifically for facial analysis tasks. Let's delve into an overview and practical implementation of DeepFace for face emotion recognition in Python.

Overview of DeepFace:

DeepFace emerges as a Python library leveraging Keras and TensorFlow, providing a comprehensive suite of facial analysis functionalities. These include face detection, facial recognition, facial attribute analysis, and the focal point, emotion recognition. Its backbone lies in deep learning models, ensuring efficient performance across various facial analysis tasks.

Installation:

Before diving into utilizing DeepFace, installing the library is the primary step, which can be easily done via pip:

pip install deepface

Getting Started:

To initiate facial emotion recognition using DeepFace, follow these steps:

Importing Necessary Libraries:

import cv2

import matplotlib.pyplot as plt

from deepface import DeepFace

Loading Image:

img = cv2.imread('happy.jpg')  # Load the image

Analyzing the Face:

predict = DeepFace.analyze(img)  # Analyze the face using the DeepFace library

Printing Prediction:

print("Emotion:", predict[0]['dominant_emotion'])  # Print the dominant emotion predicted

Real-Time Emotion Detection:

Implementing real-time emotion detection involves a few additional steps:

• Open Webcam
• Analyze the Face
• Draw Rectangle

Display Emotion Beside Webcam:

import cv2

from deepface import DeepFace

# Open webcam

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

cap = cv2.VideoCapture(1)

if not cap.isOpened():

    cap = cv2.VideoCapture(0)

if not cap.isOpened():

    raise IOError("Cannot open Webcam")

# Read face

while True:

    ret, frame = cap.read()

    result = DeepFace.analyze(frame)  # Analyze face

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    faces = face_cascade.detectMultiScale(gray, 1.1, 4)

    for (x, y, w, h) in faces:

        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    font = cv2.FONT_HERSHEY_SIMPLEX  # Font type

    cv2.putText(frame, result[0]['dominant_emotion'], (0, 50), font, 2, (0, 255, 0), 3, cv2.LINE_4)

    cv2.imshow('Demo video', frame)

    

    if cv2.waitKey(2) & 0xFF == ord('q'):

        break

cap.release()

cv2.destroyAllWindows()

Benefits of Using DeepFace

Using DeepFace for facial emotion recognition offers a myriad of benefits:

• Pre-trained Models: DeepFace provides pre-trained models trained on large datasets, ensuring accurate emotion detection.
• Ease of Use: With an intuitive API, DeepFace simplifies the process of facial emotion recognition.
• Efficiency: Leveraging efficient deep learning frameworks, DeepFace enables fast computations, even in real-time scenarios.
• Customization: DeepFace allows customization and fine-tuning of models to cater to specific use cases.
• Integration: Easily integrate DeepFace into existing Python applications or workflows.
• Community Support: Benefit from an active community providing assistance and resources.
• Real-world Applications: DeepFace finds applications in diverse fields, from market research to healthcare and security.

Conclusion

DeepFace presents a user-friendly approach to facial emotion recognition in Python, albeit the necessity for fine-tuning depending on specific requirements. With its robust capabilities and ease of integration, DeepFace opens doors to various real-world applications, marking a significant advancement in the domain of facial analysis.