The solution correctly maps the four fundamental tasks of facial recognition:

1. Verification: This is a one-to-one (1:1) matching process that confirms if two face images belong to the same person.
2. Similarity: This is a one-to-many (1:N) search that finds other faces in a collection that look similar to a query face, even if they are not the same person.
3. Grouping: This task takes a collection of unknown faces and organizes them into clusters based on visual similarity, grouping faces that likely belong to the same person.
4. Identification: This is a one-to-many (1:N) search that compares a query face against a database of known identities to find a match and determine "who" the person is.

Microsoft. (2024). Overview of the Face API. Azure AI services documentation. Retrieved October 24, 2025.

Location: Section: "Face detection and attributes."

Citation: This official documentation defines the core tasks. It explicitly states: "Face verification... checks the likelihood that two faces belong to the same person... This is a 1:1 matching." It defines identification as a "1:N matching" to find a match from a "person group" (a set of known people). It defines grouping as organizing "an unknown set of faces into several groups based on similarity." Finally, it defines "Find similar" as finding "similar-looking faces... from a face list or large face list."

Wang, M., & Deng, W. (2021). Deep Face Recognition: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 5713-5734.

DOI: https://doi.org/10.1109/TPAMI.2021.3083526

Location: Section 2, "Problem Setting."

Citation: This academic survey paper defines the standard problems in facial recognition. It distinguishes "Face Verification" as a 1:1 problem ("determine whether a pair of images belong to the same identity") and "Face Identification" as a 1:N problem ("search for a query face... from a gallery set"). This supports the distinction between the first and last questions.

Karpathy, A., & Fei-Fei, L. (2017). CS231n: Convolutional Neural Networks for Visual Recognition. Stanford University.

Location: Lecture 11 Notes: "Face Recognition."

Citation: This university courseware clarifies the common tasks. It describes verification (a 1:1 check, e.g., "is this the same person?") and identification (a 1:N search against a known database, e.g., "who is this?"). This aligns with the provided solution's mapping.