1. A computer-implemented method, comprising: determining, by a computing system, a face portion in a 2D image based at least in part on a first support vector regressor trained to predict point configurations; determining, by the computing system, a set of fiducial points in a 2D face image that was generated by transforming the face portion of the 2D image, wherein at least some fiducial points correspond to facial features, and wherein the set of fiducial points are localized based at least in part on a second support vector regressor that is different from the first support vector regressor; generating, by the computing system, a 3D-aligned face image from the 2D face image based at least in part on the set of fiducial points detected in the 2D face image and a corresponding set of anchor points placed in a 3D shape of a human face; and classifying, by the computing system, a facial identity corresponding to the 2D face image based on provision of the 3D-aligned face image to a deep neural network (DNN), wherein the DNN is trained to classify the 2D face image based on a set of features of the 3D-aligned face image.