<p>Actually, for giving you a proper answer, I'd be happy to know some details of your task and your data. Face Recognition is a non-trivial problem and there is no general solution for all sorts of image acquisition.</p>
<p>First of all, you should define how many sources of variation (posing, emotions, illumination, occlusions or time-lapse) you have in your sample and testing sets. Then you should choose an appropriate algorithm and, very importantly, preprocessing steps according to the types.</p>
<p>If you don't have any significant variations, then it is a good idea to consider for a small training set one of the <a href="https://ieeexplore.ieee.org/document/5605541" rel="nofollow noreferrer">Discrete Orthogonal Moments</a> as a feature extraction method. They have a very strong ability to extract features without redundancy. Some of them (Hahn, Racah moments) can also work in two modes - local and global feature extraction. The topic is relatively new, and there are still few articles about it. Although, they are thought to become a very powerful tool in Image Recognition. They can be computed in near real-time by using recurrence relationships. For more information, have a look <a href="https://ieeexplore.ieee.org/document/941859" rel="nofollow noreferrer">here</a> and <a href="http://www.researchgate.net/publication/5606888_Image_analysis_by_Krawtchouk_moments/file/72e7e52a11c6fd7f2b.pdf" rel="nofollow noreferrer">here</a>.</p>
<p>If the pose of the individuals significantly varies, you may try to perform firstly pose correction by <a href="https://link.springer.com/chapter/10.1007/978-3-540-79547-6_51" rel="nofollow noreferrer">Active Appearance Model</a>.</p>
<p>If there are lots of occlusions (glasses, hats) then using one of the <a href="https://arxiv.org/ftp/arxiv/papers/0907/0907.4984.pdf" rel="nofollow noreferrer">local feature extractors</a> may help.</p>
<p>If there is a significant time lapse between train and probe images, the local features of the faces could change over the age, then it's a good option to try one of the algorithms which use <a href="https://ieeexplore.ieee.org/document/5634496" rel="nofollow noreferrer">graphs for face representation</a> so as to keep the face topology.</p>
<p>I believe that non of the above are implemented in OpenCV, but for some of them you can find <a href="https://www.mathworks.com/matlabcentral/fileexchange/26706-active-shape-model-asm-and-active-appearance-model-aam" rel="nofollow noreferrer">MATLAB implementation</a>.</p>
<p>I'm not native speaker as well, so sorry for the grammar</p>
Face recognition with a small number of samples
<p>Can anyone advise me way to build effective face classifier that may be able to classify many different faces (~1000)?</p>

<p>And i have only 1-5 examples of each face</p>

<p>I know about opencv face classifier, but it works bad for my task (many classes, a few samples). 
It works alright for one face classification with small number of samples. But i think that 1k separate classifier is not good idea</p>

<p>I read a few articles about face recognition but methods from these articles reqiues a lot of samples of each class for work </p>

<p>PS Sorry for my writing mistakes. English in not my native language.</p>