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However, if you tried to simply perform face recognition directly on a normal photo image, you will probably get less than 10% accuracy!

It is extremely important to apply various image pre-processing techniques to standardize the images that you supply to a face recognition system.

You can actually use this function with any of these other detectors if you want, or even create your own custom detector such as for car or person detection (read here), but since frontal face detection is the only one that is very reliable, it is the only one I will discuss.

For frontal face detection, you can chose one of these Haar Cascade Classifiers that come with Open CV (in the "data\haarcascades\" folder): Each one will give slightly different results depending on your environment, so you could even use all of them and combine the results together (if you want the most detections).

My new code also does better image preprocessing & rotation than the code on this page, but the book code doesn't have an offline recognition mode.

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However, Face Recognition is much less reliable than Face Detection, generally 30-70% accurate.

Face Recognition has been a strong field of research since the 1990s, but is still far from reliable, and more techniques are being invented each year such as the ones listed at the bottom of this page (Alternatives to Eigenfaces such as 3D face recognition or recognition from video).

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