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Face Recognition MCQ
Detect faces, align, embed into a metric space—then verify identity or search galleries.
Face
Identity
Embedding
L2 space
Verify
Same person?
Identify
1:N gallery
Face recognition pipeline
Modern systems detect faces, align landmarks, crop, and map to an embedding vector with a CNN trained via metric objectives (triplet, contrastive) or softmax variants. Verification compares two embeddings with a threshold; identification searches a gallery (1:N). Fairness, spoofing, and privacy are active concerns.
Metric space
Same-identity pairs should be closer in cosine or L2 distance than different identities.
Key ideas
Detection + alignment
Find face and warp to canonical pose before embedding.
Embedding
Compact vector representing identity-discriminative features.
Verification
1:1 decision: same or different person.
Identification
Match probe to gallery (closed or open set).
Scoring
cosine similarity or L2 distance vs learned threshold