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Extract Face Embedding from an Image

Source: R/embeddings.R
get_embedding.Rd

Extracts a deep learning embedding vector from a face image using a specified model. The embedding can be used to compute face similarity.

Usage

get_embedding(
  img_path,
  model = "ArcFace",
  detector = "retinaface",
  align = TRUE,
  enforce_detection = TRUE,
  expand_percentage = 0
)

Arguments

img_path

Path to the image file containing a face.

model

Character. The face recognition model to use. Default is "ArcFace" (recommended). See available_models.

detector

Character. Face detection backend. Default is "retinaface" (most accurate). See available_detectors.

align

Logical. Whether to align the face before embedding. Default is TRUE (recommended).

enforce_detection

Logical. If TRUE, raises an error if no face is detected. If FALSE, attempts to use the entire image. Default is TRUE.

expand_percentage

Numeric. Percentage to expand the detected face region. Default is 0.

Value

A named list containing:

  • embedding - Numeric vector of face embeddings

  • facial_area - List with x, y, w, h of detected face region

  • face_confidence - Detection confidence score

Details

The embedding dimension depends on the model:

  • ArcFace: 512 dimensions

  • Facenet512: 512 dimensions

  • Facenet: 128 dimensions

  • VGG-Face: 4096 dimensions

One example configuration used in the package is:

  • model = "ArcFace"

  • detector = "retinaface"

  • align = TRUE

These defaults do not establish superior accuracy for every population, image source or research question, or validate lineup fairness.

Examples

if (FALSE) { # \dontrun{
# Get embedding for a single face
emb <- get_embedding("path/to/face.jpg")

# Access the embedding vector
emb$embedding

# Use a different model
emb_facenet <- get_embedding("path/to/face.jpg", model = "Facenet512")
} # }