Python Facial Emotion Recognition using Keras

Facial emotion recognition is a technology that allows computers to identify and interpret human emotions from facial expressions. It has a wide range of applications, including market research, human-computer interaction, and mental health monitoring. In this article, we will explore how to build a facial emotion recognition system using Python and Keras, a popular open-source deep learning library.

Prerequisites

Before getting started with the code, make sure you have the following installed on your machine:

• Python 3
• Keras
• OpenCV
• Facial recognition dataset (e.g. FER2013)

Building the Model

To build a facial emotion recognition model, we will first need to create a deep learning model using Keras. We can use a convolutional neural network (CNN) to classify facial expressions into different emotion categories. Here’s a basic example of how to create a simple CNN using Keras:

model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax')
		

Training the Model

Once we have built the model, we can train it using a facial expression dataset. The FER2013 dataset, for example, contains images of facial expressions categorized into 7 different emotions. We can use this dataset to train our model to recognize these emotions. Here’s an example of how to train the model using the FER2013 dataset:

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(X_train, y_train, batch_size=32, epochs=10, validation_data=(X_val, y_val))
		

Testing the Model

After training the model, we can test it on new images to see how accurately it can recognize facial expressions. We can use OpenCV to capture live video from a webcam and feed it into the model for real-time emotion recognition. Here’s an example of how to test the model using live video:

# Capture live video from webcam
cap = cv2.VideoCapture(0)

while True:
	ret, frame = cap.read()
	# Preprocess the image
	# Feed the preprocessed image into the model
	# Display the recognized emotion on the video
		

Conclusion

Facial emotion recognition is a powerful technology with a wide range of applications. With Python and Keras, it is relatively easy to build and train a facial emotion recognition model. By leveraging deep learning and computer vision techniques, we can create systems that are capable of accurately identifying and interpreting human emotions from facial expressions.