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Download the Labeled Faces in the Wild dataset, which is available on the official website of the University of Massachusetts Amherst.

Extract the images and labels from the dataset.

Preprocess the dataset, including image resizing, normalization, and cropping to the face region.

Split the dataset into training and testing sets.

Define and compile the CNN model in Keras. The model should include convolutional and pooling layers, followed by fully connected layers, and the output should be a binary classification.

Train the model with the training set. Set the number of epochs and batch size.

Evaluate the model on the testing set, and calculate the accuracy and loss.

Fine-tune the model if necessary, by adjusting the hyperparameters or modifying the architecture.

Once the model is trained and tested, use it for face verification by comparing the similarity between images. The output should indicate whether the two images are of the same person or not.

Deploy the model in a real-world environment, by integrating it into a web or mobile application for face verification.