Are Face Embeddings Compatible Across Deep Neural Network Models?

1. 1
   Identify Diverse Face Embedding Models

   Select a range of Deep Neural Network (DNN) models, including specialized face recognition models and general-purpose foundation models, known for generating face embeddings.
2. 2
   Generate Embeddings from Sample Data

   Using a standardized dataset of facial images, generate face embeddings for each chosen model. Ensure consistent input processing for fair comparison.
3. 3
   Define & Apply Compatibility Metrics

   Establish quantitative metrics (e.g., cosine similarity, Euclidean distance, clustering performance) to assess the similarity and interchangeability of embeddings produced by different models. Apply these metrics across all generated embeddings.
4. 4
   Evaluate Cross-Model Performance

   Test the practical compatibility by using embeddings from one model within a downstream task (e.g., verification, identification) typically handled by another model. Measure the performance impact (e.g., accuracy, false positive rate).
5. 5
   Inform System Design & Integration

   Analyze the compatibility findings to guide decisions on model selection, fine-tuning strategies, and the feasibility of integrating outputs from disparate AI components in your face recognition systems.

import numpy as np

def get_dummy_face_embedding(image_data: np.ndarray) -> np.ndarray:
    """Generates a dummy 512-dimensional face embedding."""
    # In a real scenario, this would involve a DNN inference call
    # e.g., model.predict(preprocess_image(image_data))
    return np.random.rand(512).astype(np.float32)

# Example usage:
dummy_image = np.random.rand(128, 128, 3) # Simulate a 128x128 RGB image
embedding_model_A = get_dummy_face_embedding(dummy_image)
embedding_model_B = get_dummy_face_embedding(dummy_image) # Simulate another model's output

print(f"Embedding from Model A (first 5 dims): {embedding_model_A[:5]}")
print(f"Embedding from Model B (first 5 dims): {embedding_model_B[:5]}")

# Calculate cosine similarity (a common compatibility metric)
def cosine_similarity(vec1, vec2):
    return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))

similarity = cosine_similarity(embedding_model_A, embedding_model_B)
print(f"Cosine Similarity between A and B: {similarity:.4f}")

• Paperarxiv.org