Facial Emotion Recognition
This article begins by establishing the foundation of the project, outlining the essential Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn.
These tools are instrumental for data manipulation and visualization, setting the stage for the subsequent steps in the machine learning pipeline. The focus is on preprocessing and preparing the ‘fer2013.csv’ dataset, which is pivotal for training the model. This dataset comprises labeled images representing a range of human emotions, and the initial steps involve data exploration and visualization to understand the distribution and characteristics of these emotional labels.
The core of the article centers on the construction and training of a Convolutional Neural Network (CNN), utilizing the Keras framework. The process of building the sequential model is detailed, highlighting the incorporation of various layers, such as convolutional, pooling, and dropout layers. These are crucial for feature extraction and learning from the facial expression data. Alongside model architecture, the article emphasizes the significance of data augmentation techniques to enhance the model’s ability to generalize across different facial expressions and conditions. Further, it covers the compilation, training, and optimization of the model, including strategies like early stopping and adaptive learning rates to refine the training process. The culmination of these steps is the application phase, where the model is employed to predict emotions from new facial images, using a Haar cascade classifier for face detection and linking the predicted emotions to corresponding emojis. This practical demonstration underscores the model’s applicability and effectiveness in real-world scenarios.
Let’s start coding:
""" IMPORT ALL DEPENDENCIES"""
# NumPy for numerical computing
import numpy as np
# Pandas for DataFrames
import pandas as pd
# Matplotlib for visualization
from matplotlib import pyplot as plt
# display plots in the notebook
%matplotlib inline
# import color maps
from matplotlib.colors import ListedColormap
# Seaborn for easier visualization
import seaborn as sns
This code imports all the dependencies that are needed for the code to run correctly. First, it imports NumPy which is used for numerical computing. Then it imports Pandas which allows for the creation and manipulation of dataframes. Next, it imports Matplotlib which is used for data visualization. This line %matplotlib inline ensures that the plots are displayed directly in the notebook. The code then imports color maps from Matplotlib for further customization of the plots. Finally, it imports Seaborn which is a data visualization library that provides an easier way to create and customize plots.
df = pd.read_csv("/root/.kaggle/fer2013/fer2013.csv")
df.head()
This code involves loading a CSV file from a directory called /root/.kaggle/fer2013, specifically the fer2013.csv file. pd.read_csv is used to read the file and return a DataFrame, in this case stored in the variable df. The data within the DataFrame is then displayed using the df.head method, which shows the first five rows of the DataFrame. Essentially, this code is loading and displaying the data from the fer2013.csv file.