Gender and Age Detection using CNN and SVM classifier | DL Project.

Summary

This video tutorial covers a project on gender and age detection using machine learning. The presenter demonstrates the process of loading a dataset from Kaggle, applying data preprocessing, and implementing two models: a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM). The CNN model, featuring three convolutional layers and two pooling layers, achieves a validation accuracy of 78%. The SVM model is also applied for classification tasks. Throughout the video, the presenter showcases sample outputs of gender and age predictions from both models, explaining the steps involved in training and evaluating each model.

Takeaways

• 😀 The task involves gender and age detection from images using machine learning models.
• 😀 The dataset was downloaded from Kaggle and contains a variety of images used for training and testing.
• 😀 The images are preprocessed and categorized into gender (male and female) and age groups.
• 😀 A random image from the dataset is visualized to ensure correct loading and indexing.
• 😀 A CNN model was built with three convolutional layers, two pooling layers, and dropout to prevent overfitting.
• 😀 The CNN model was trained with an 80-20 split between training and test data, achieving an accuracy of 78%.
• 😀 The model's performance was assessed by displaying the predicted gender and age against the actual values for test images.
• 😀 The SVM classifier was also used as a second model for gender and age prediction, using a similar train-test split.
• 😀 The SVM model predicted gender (male/female) and age groups with varying accuracy.
• 😀 The script includes a final visualization step where predicted labels for gender and age are compared with actual labels for several test images.
• 😀 Both CNN and SVM models showed reasonable performance but may benefit from further optimization or additional data.

Q & A

What was the primary task described in the script?

The primary task was gender and age detection from a dataset of images using machine learning models like CNN and SVM.

Which dataset was used for the task?

The dataset was downloaded from Kaggle, which provided images for gender and age classification.

How was the data processed before applying machine learning models?

The data was loaded from the Kaggle dataset, and images were displayed randomly to ensure proper labeling. The images were also converted to grayscale for uniformity during model training.

What were the gender and age labels used in the dataset?

The gender label was `0` for male and `1` for female. Age was categorized into various levels corresponding to different age groups.

What machine learning models were used for the task?

The two main models used were Convolutional Neural Networks (CNN) for detecting both gender and age separately, and Support Vector Machine (SVM) classifiers for the same task.

What was the architecture of the CNN model used?

The CNN model had three convolution layers, two pooling layers, and a dropout layer to prevent overfitting. It was used separately for age and gender classification.

What was the performance of the CNN model?

The CNN model achieved an accuracy of approximately 78% for gender and age detection.

How was the dataset split for training and testing the models?

The dataset was split into 80% for training and 20% for testing. The models were then trained on the training data and evaluated on the testing data.

What did the SVM model do differently compared to the CNN model?

The SVM model used different classification techniques and was trained separately for gender and age, providing predictions for gender and approximate age ranges.

What was the overall result of using the CNN and SVM models?

Both models successfully detected gender and age from the images. The CNN model showed better performance with 78% accuracy, while the SVM model also provided useful predictions but was less detailed in categorizing age.