Face identification method based on non-negative matrix factorization and a plurality of distance functions

Abstract

The invention discloses a face identification method based on non-negative matrix factorization and a plurality of distance functions. The method comprises the following steps of: extracting face image characteristics, performing the non-negative matrix factorization on all face images in a learning image library to obtain a corresponding basic image and a weight vector, namely a characteristic vector corresponding to each learning image; performing characteristic extraction on all of test images by utilizing a non-negative matrix factorization algorithm to obtain a characteristic vector corresponding to each test image; calculating a mean characteristic vector Hm corresponding to each type of training sample image set with known identity by utilizing the obtained characteristic vectors; and calculating similarity between each test image and the mean vector corresponding to each type of training sample image set, obtaining a quantized numerical value of the similarity by combining the different distance functions, finding a training image type which is proximate to the test image, namely the nearest neighbor point, and classifying the test image into a type with the nearest neighbor point according to a nearest neighbor classification method to finish the identification of all the test images.

Description

technical field [0001] The invention relates to the application field of image processing and pattern recognition technology, in particular to an image recognition method based on non-negative matrix factorization (NMF) and various distance functions. Background technique [0002] In the field of face recognition, there are currently many methods, which can be roughly divided into two categories: unsupervised recognition methods and supervised recognition methods. The unsupervised recognition method does not impose any prior knowledge on the classification process in advance, but only uses the features extracted from the samples to be classified to establish decision rules for classification, mainly including principal component analysis (PCA), non-negative matrix decomposition ( NMF). [0003] The basic method of principal component analysis (PCA) is as follows: [0004] Let X={X first n ∈R d |n=|1,...,N} is a set of vectors, that is, a data set. [0005] Then, the cor...

AI Technical Summary

Problems solved by technology

But traditional eigenface methods have their limitations

First, the base image for PCA lacks intuition

Secondly, this method is based on the

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