eigenfaces algorithme

Question

Je suis la programmation d'un programme de reconnaissance de visage en utilisant OpenCV.

génération eigenfaces:

• dois-je utiliser une grande base de données de visages inconnus?
• dois-je utiliser seulement des photos des gens que je veux que mon système reconnaisse?
• dois-je utiliser à la fois?

Je parle de la génération de eigenfaces, c'est le " apprentissage " étape.

Et combien de photos dois-je utiliser pour avoir une précision décente? Plus de 20, ou 2000?

Merci

Answer 1

Eigenfaces works by projecting the faces into a particular "face basis" using principal component analysis or PCA. The basis does not have to include photos of people you want to recognize.

Instead, I would encourage you to train based upon a big database (at least 10k faces) that is well registered (eigenfaces doesn't work well with images that are shifted). The original paper by Turk and Pentland was remarkable partly due to the large pin registered face database they released. I would also say that try to have the lighting normalized to the same between the database and your test inputs.

In terms of testing, first 20 components should be sufficient to reconstruct a human recognizable face and first 100 components should be enough to discriminate between any two face for essentially arbitrarily large dataset.

Answer 2

You don't need too many random faces to compose a human face; somewhere close to 20 should give good results, maybe go with more if you can. They should all be lined up as much as possible to one another, front facing, and photos in grayscale under the same lighting conditions.