Efficient Statistical Face Recognition Using Trigonometric Series and CNN Features
In this paper we deal with unconstrained face recognition with few training samples. The facial images are described with the off-the shelf high-dimensional features extracted with a deep convolutional neural network (CNN), which was preliminarily trained with an external very-large dataset. We focus on drawbacks of conventional probabilistic neural network (PNN), namely, low recognition performance and high memory space complexity. We propose to modify the PNN by replacing the exponential activation function in the Gaussian Parzen kernel to the trigonometric functions and use the orthogonal series density estimation of the CNN features. We demonstrate that the proposed approach significantly decreases the runtime complexity of face recognition if the classes are rather balanced and there are more than five training images per each subject. An experimental study with either traditional VGGNet and Light CNN, or contemporary VGGFace2_ft and MobileNet trained on VGGFace-2 dataset, have shown that our algorithm is very efficient and rather accurate in comparison with the instance-based learning classifiers.