Given a short query with keyphrases about a research idea in 2015 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: We present a simple and efficient framework for face frontalization based on the observation that faces appear similar

Keyphrases: face frontalization images

Dataset description:
UMIST: The Sheffield (previously UMIST) Face Database consists of 564 images of 20 individuals (mixed race/gender/appearance). Each individual is shown in a range of poses from profile to frontal views – each in a separate directory labelled 1a, 1b, … 1t and images are numbered consecutively as they were taken.
CUFS: CUHK Face Sketch database (CUFS) is for research on face sketch synthesis and face sketch recognition. It includes 188 faces from the Chinese University of Hong Kong (CUHK) student database, 123 faces from the AR database [1], and 295 faces from the XM2VTS database [2].
Adience: The Adience dataset, published in 2014, contains 26,580 photos across 2,284 subjects with a binary gender label and one label from eight different age groups, partitioned into five splits. The key principle of the data set is to capture the images as close to real world conditions as possible, including all variations in appearance, pose, lighting condition and image quality, to name a few.
WMCA: The Wide Multi Channel Presentation Attack (WMCA) database consists of 1941 short video recordings of both bonafide and presentation attacks from 72 different identities. The data is recorded from several channels including color, depth, infra-red, and thermal.

Options:- A: UMIST  B: CUFS  C: Adience  D: WMCA
Adience