Hi Kartikeya,
I understand that you are looking out for a Deep Learning Technique for age or Emotion classification.
You would typically use Convolutional Neural Networks (CNNs) which are very effective for image classification tasks. You can use pre-trained models or train your own model using a dataset like the Facial Expression Recognition (FER) dataset.
For age detection, you would need a different dataset that is labeled with the ages of the individuals, such as the IMDB-WIKI dataset.
You can download FER dataset from Kaggle: https://www.kaggle.com/datasets/msambare/fer2013
Here is a basic outline of steps for emotion recognition with the FER dataset:
- Load and Preprocess Data: Load the FER dataset, normalize the pixel values, and convert the labels to categorical format.
- Build CNN Model: Define a CNN architecture with layers suitable for image classification.
- Compile the Model: Specify the optimizer, loss function, and evaluation metrics.
- Train the Model: Fit the model to the training data using the specified options.
- Evaluate the Model: Test the model's accuracy on a separate test set.
For age detection, you would need a similar approach but with a dataset that includes age labels, and you might need to adjust the model's output layer to suit the age prediction task.
