Age and Gender Detection Using Classification and Regression | Deep Learning Project

Summary

This project demonstrates an age and gender detection system using deep learning. By utilizing the UTK Face dataset, the system extracts features from images to predict both gender (binary classification) and age (regression). The convolutional neural network (CNN) is designed with layers to process images, including dropout to prevent overfitting. The model is trained for 50 epochs, with performance evaluated through accuracy for gender and loss for age. Predictions on new images showcase the model's capability, and the source code is available for further exploration. The project combines classification and regression for effective human attribute prediction.

Takeaways

• 😀 The project aims to predict both gender and age from an image using a neural network model.
• 😀 The required libraries include numpy, os, matplotlib, seaborn, tensorflow, and others for data handling, visualization, and model creation.
• 😀 The UTKFace dataset, consisting of 23,000 images with labels for age, gender, and ethnicity, is used in this project.
• 😀 Image file names are shuffled to ensure randomness during exploratory data analysis and prevent sequential plotting.
• 😀 Age and gender are extracted from the image file names, where age is the first component and gender the second.
• 😀 The gender is encoded as binary (0 for male and 1 for female) for classification, while age is treated as a numerical value for regression.
• 😀 Exploratory data analysis (EDA) includes displaying random images, plotting the age distribution, and visualizing a range of ages and genders.
• 😀 Custom feature extraction functions load images, resize them to 128x128 pixels, convert them to grayscale, and store them as numpy arrays.
• 😀 The CNN architecture consists of convolutional layers, max-pooling layers, dense layers, and dropout layers to prevent overfitting.
• 😀 The model has two output layers: one for gender classification (using sigmoid activation) and one for age prediction (using linear activation).
• 😀 The model uses binary cross-entropy loss for gender and mean absolute error (MAE) for age prediction during training.
• 😀 The neural network is trained for 50 epochs, with early stopping recommended around 10-15 epochs to avoid overfitting.
• 😀 Model performance is evaluated using the loss and accuracy curves for gender prediction and MAE for age prediction.
• 😀 The project is available for download via the repository, including the code and test images for further exploration.

Q & A

What is the main goal of the age and gender detection system described in the project?

The main goal of the system is to predict the gender and estimate the age of individuals based on a given image using a convolutional neural network (CNN).

What libraries are necessary to set up the age and gender detection system?

The necessary libraries include 'load_image' for loading images, 'sequential' and 'model' for building the neural network, layers like 'Dense', 'Conv2D', 'Dropout', 'Flatten', and 'MaxPooling2D' for constructing the network, and 'OS', 'matplotlib', 'seaborn', and 'tqdm' for dataset manipulation and visualization.

How is the dataset prepared for use in the model?

The UTKFace dataset, containing labeled images with age, gender, and ethnicity, is unzipped and processed. The image filenames are shuffled and split to extract the age and gender labels, which are then converted into integers. The data is stored in a DataFrame for easy manipulation.

Why is the age distribution skewed to the right in the dataset?

The age distribution is skewed to the right because there are more younger individuals in the dataset, and outliers exist at the higher end of the age spectrum.

What is the significance of converting images to grayscale for the neural network?

Converting images to grayscale reduces computational resources by eliminating the need to process three color channels (RGB), allowing the network to focus on essential features while conserving memory and processing power.

Why is the mean absolute error (MAE) used as the loss function for age estimation?

The mean absolute error (MAE) is used for age estimation because age is a continuous variable, and MAE measures the average magnitude of errors in predictions, which is suitable for estimating numerical values like age.

What is the purpose of using a dropout layer in the neural network?

The dropout layer is used to prevent overfitting by randomly setting a fraction of the input units to zero during training, which helps the model generalize better and avoid memorizing the training data.

Why are there two separate output layers in the network?

There are two separate output layers because the problem requires both gender prediction (binary classification) and age estimation (continuous regression). One output uses a sigmoid activation for gender, while the other uses a linear activation for age.

What is the impact of overfitting in the training process?

Overfitting occurs when the model performs well on training data but poorly on unseen data, often because it has memorized the details of the training set instead of generalizing patterns. Early stopping is suggested to avoid overfitting and improve generalization.

How are the final predictions evaluated, especially for age estimation?

For gender, accuracy is used to evaluate the model, while for age, the model's performance is assessed based on the loss (mean absolute error), as age is a continuous variable and accuracy does not make sense in this context.