Yea, so at Lambda, depending on the application, we use either convolutional neural networks or layered neural networks initialized with unsupervised training using autoencoders to do the feature extraction from images. The features are either passed through a softmax layer or used as a "siamese" network. (Siamese networks provide a similarity metric as opposed to a probability distribution over labels.[3])
As far as recognition goes, most modern algorithms don't use fiducials (what you call 'nodal points') to do recognition. So there's nothing in our code that has to do with interocular distance or mouth width, etc., so all of the features are "learned". However, fiducials are used to do a frontalization or re-alignment step before passing it through the feature extractor (neural network or otherwise). This type of frontalization step allowed Facebook's deepface algorithm to use locally learned filters for their CNN as opposed to shared weights.[1][2]
As far as recognition goes, most modern algorithms don't use fiducials (what you call 'nodal points') to do recognition. So there's nothing in our code that has to do with interocular distance or mouth width, etc., so all of the features are "learned". However, fiducials are used to do a frontalization or re-alignment step before passing it through the feature extractor (neural network or otherwise). This type of frontalization step allowed Facebook's deepface algorithm to use locally learned filters for their CNN as opposed to shared weights.[1][2]
[1] http://www.cv-foundation.org/openaccess/content_cvpr_2014/pa...
[2] http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf
[3] http://yann.lecun.com/exdb/publis/pdf/chopra-05.pdf