You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: I want to use StyleGAN to create a large, high-quality dataset of facial images.

Keyphrases: style transfer images

Dataset description:
MegaFace: MegaFace was a publicly available dataset which is used for evaluating the performance of face recognition algorithms with up to a million distractors (i.e., up to a million people who are not in the test set). MegaFace contains 1M images from 690K individuals with unconstrained pose, expression, lighting, and exposure.
FRLL-Morphs: FRLL-Morphs is a dataset of morphed faces based on images selected from the publicly available Face Research London Lab dataset [1]. We created the database by selecting similar looking pairs of people, and made 4 types of morphs for each pair using the following morphing tools: OpenCV [2], FaceMorpher [3], StyleGAN 2 [3], WebMorpher [4].
CASIA-WebFace: The CASIA-WebFace dataset is used for face verification and face identification tasks. The dataset contains 494,414 face images of 10,575 real identities collected from the web.
4DFAB: 4DFAB is a large scale database of dynamic high-resolution 3D faces which consists of recordings of 180 subjects captured in four different sessions spanning over a five-year period (2012 - 2017), resulting in a total of over 1,800,000 3D meshes. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours.
BlendedMVS: BlendedMVS is a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. The dataset was created by applying a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes.

Options:- A: MegaFace  B: FRLL-Morphs  C: CASIA-WebFace  D: 4DFAB  E: BlendedMVS
["MegaFace", "CASIA-WebFace"]