Contents
How data augmentation can be useful?
Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks.
Where can data augmentation be used?
Data augmentation can be used to address both the requirements, the diversity of the training data, and the amount of data. Besides these two, augmented data can also be used to address the class imbalance problem in classification tasks.
Why is image augmentation needed?
Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Image data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize.
Are there any data augmentation methods for face recognition?
Face image has its own particularity and the main challenges for face recognition including poses, illumination, occlusion, etc. The previous common used data augmentation methods, which just make some simple transformations, cannot handle these problems. Hence, face specified data augmentation methods have been proposed.
How can data augmentation be applied to an image?
Now, let’s dive into the details of the data augmentation techniques and apply them on our image. We can rotate the image by applying some angle. Each rotated image is a unique one to the model. The rotation can be applied up to 360 degrees based on the object in the image.
How is data augmentation used in machine learning?
You have a stellar concept that can be implemented using a machine learning model. Feeling ebullient, you open your web browser and search for relevant data. Chances are, you find a dataset that has around a few hundred images. You recall that most popular datasets have images in the order of tens of thousands (or more).
Which is the best method for face recognition?
Jiang et al. [22] proposed an efficient 3D reconstruction method to generate face images with different poses, illuminations and expressions. Mohammadzade and Hatzinakos [23] proposed an expression subspace projection method to synthesize new expression images for each person.