Face Recognition Using Invariance with a Single Training Sample
Abstract: For the limits of
memories and computing performance of current intelligent terminals, it is necessary
to develop some strategies which can keep the balance of the accuracy and
running time forface recognition. The purpose of the work in this paper is to
find the invariant features of facial images and represent each subject with
only one training sample for face recognition. We propose a two-layer hierarchical
model, called invariance model, and its corresponding algorithms to keep the
balance ofaccuracy, storage and running time. Especially, we take advantages of
wavelet transformations andinvariant moments to obtain the key features as well
as reduce dimensions of feature data based on thecognitive rules of human
brains. Furthermore, we improve usual pooling methods, e.g. max pooling and average
pooling, and propose the weighted pooling method to reduce dimensions with no
effect onaccuracy, which let storage requirement and recognition time greatly
decrease. The simulation results show that the proposed method does better than
some typical and nearly-proposed algorithms in balancing the accuracy and
running time.
Author: Qian Tian
Journal Code: jptkomputergg140131
