얼굴 인식
두 얼굴이 같은 사람인지 판정한다
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이 능력이 뜻하는 것
Takes one or two face images and outputs an identity decision: verification answers whether the two are the same person, while identification asks who in a gallery a face matches. Faces are first aligned and encoded into feature vectors, then compared by distance. Unlike classification the class set is not fixed; the comparison target comes from a dynamic gallery.
기술적으로 구현하는 방법
The pipeline typically detects a face, aligns it by keypoints, and extracts a feature vector with a deep network trained by metric learning to pull the same person together and push others apart. Early systems relied on hand-crafted features; deep metric learning around 2014 pushed accuracy past the practical threshold. Liveness detection, which separates a live person from a photo or a mask, is an indispensable part of any deployment.
대표 제품
1관련 기관
대표적 용도
- Access control and ID verification
- Automatic people grouping in photo albums
- Identity confirmation for remote account opening
- Video search and person tracking
성능을 평가하는 방법
- 1:1 verification accuracy
- Correct same-or-different decisions
- TAR@FAR
- True accept rate at a fixed false accept rate
- Identification mAP
- Ranking quality when searching a gallery
경계와 난점
- Large pose differences, age gaps and harsh lighting push the same person apart in feature space
- Uneven training data produces systematic bias across skin tones and genders
- Photos, replayed video and realistic masks can fool the system, so liveness detection is required, not optional