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: We conduct a comparative evaluation of several state-of-the-art deformable face tracking methods.

Keyphrases: deformable face tracking video

Dataset description:
300W: The 300-W is a face dataset that consists of 300 Indoor and 300 Outdoor in-the-wild images. It covers a large variation of identity, expression, illumination conditions, pose, occlusion and face size.
CDTB: dataset is recorded by several passive and active RGB-D setups and contains indoor as well as outdoor sequences acquired in direct sunlight. The sequences were recorded to contain significant object pose change, clutter, occlusion, and periods of long-term target absence to enable tracker evaluation under realistic conditions.
CPLFW: A renovation of Labeled Faces in the Wild (LFW), the de facto standard testbed for unconstraint face verification. There are three motivations behind the construction of CPLFW benchmark as follows:  1.Establishing a relatively more difficult database to evaluate the performance of real world face verification so the effectiveness of several face verification methods can be fully justified.
AFW: AFW (Annotated Faces in the Wild) is a face detection dataset that contains 205 images with 468 faces. Each face image is labeled with at most 6 landmarks with visibility labels, as well as a bounding box.
RFW: To validate the racial bias of four commercial APIs and four state-of-the-art (SOTA) algorithms.

Options:- A: 300W  B: CDTB  C: CPLFW  D: AFW  E: RFW
["300W", "AFW"]