You can compress the images however this may lead to a worse recognition rate, due to image quality reduction and also you will have to decode on the device. Once 1 and 2 have been completed the system is very quick as it just performs recognition against the training set.įortunately for me, my database server was located on a LAN therefore speed wasn't a problem, however I can see why you have an issue due to the fact that on a mobile device you have a limited data connection (speed/bandwidth).Generate training set based on these images.Open application and grab training images from database.In my application, I would go through the following steps. The database is a particularly tricky part of the system as this is where the biggest bottleneck is. I stored all my images in the database as Blob data types then loaded them into my program when necessary. I actually did something similar to you albeit on a PC not an iPhone but its still the same concept.

You could also look at implementing it yourself if you feel brave (it's not too bad). That link shows a tutorial on how to get that working with OpenCV - I think this is written for C but i'm sure you can get the basic jist of it. I would use a method such as Principal Component Analysis (PCA) a.k.a eigenfaces. I would use the Haarcascades available in open CV to perform quick and accurate face detection.

Chessboard pdf open cv face detection