AI-102 Implement computer vision solutions Practice Question
A company uses the Face API for identity verification. During testing, they find that the similarity scores between two images of the same person are lower than expected. Which factor is most likely causing this?
⚠ Common exam trap
It's easy for candidates to assume image quality (compression or resolution) is the primary factor, but the Face API is designed to handle those variations, whereas lighting is a known sensitivity in facial recognition systems.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The images have different lighting conditions (e.g., one is brightly lit, the other is dark).
B is correct because the Face API's similarity scoring is heavily influenced by lighting conditions. Variations in illumination can alter facial features, shadows, and contrast, which reduces the accuracy of face matching algorithms. The API relies on consistent lighting to extract reliable facial landmarks and embeddings, so differing lighting conditions directly lower similarity scores.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The images are compressed with different quality levels.
Why it's wrong here
Compression may have minor impact but less than lighting.
- ✓
The images have different lighting conditions (e.g., one is brightly lit, the other is dark).
Why this is correct
Lighting changes facial appearance and reduces similarity scores.
- ✗
The backgrounds of the images are different.
Why it's wrong here
Background does not affect face comparison.
- ✗
The images have different dimensions (e.g., 500x500 vs 1000x1000).
Why it's wrong here
Face API resizes images internally; dimensions do not affect similarity.
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