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Atlas de IA

Reconocimiento facial

Decidir si dos rostros son la misma persona

Comprensión visualIntermedio #16
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El texto completo se presenta en inglés; el título y el resumen están traducidos.

QUÉ SIGNIFICA ESTA CAPACIDAD

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.

Cómo se consigue técnicamente

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.

Productos representativos

1

Organizaciones relacionadas

Usos típicos

  • Access control and ID verification
  • Automatic people grouping in photo albums
  • Identity confirmation for remote account opening
  • Video search and person tracking

Cómo se evalúa

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

Límites y dificultades

  • 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

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