Facial Feature Extraction Method Based on Sample Pair Weighting Based on lp Norm

Abstract

An Lp norm-based sample couple-weighting facial feature extraction method belongs to a feature extraction method in pattern recognition. The Lp norm-based sample couple-weighting facial feature extraction method includes the following steps: (1) n facial images, the sizes of which are M multiplied by N, are represented in the form of column vectors, wherein the dimensionality of Xi is d, and the column vectors are formed into a sample matrix; (2) different functions are adopted as weighting functions for facial sample couples of the same kind and facial sample couples of different kinds; (3) a sample couple-weighting optimization model with Lp norm constraints is created, and an iterative optimization algorithm is utilized to obtain a locally optimal unit projection vector w; and (4) a greedy algorithm is used for reducing the initial d dimensions of the features of the facial images to m dimensions, and thereby the reduction of dimensionality and the extraction of effective features are implemented. The method can flexibly carry out feature extraction on different types of data sets and decrease the sensitivity on abnormal values, and can be more adapted to the complexity of facial images; and since sample couples are weighted, the affection of sample mean is avoided, and extracted features are more effective. Under the condition of blocking, the performance of the method is enhanced by 2 to 5 percent in comparison with the performance of PCA (principal component analysis) and Lp-PCA-L1.

Description

technical field [0001] The invention relates to a feature extraction method in pattern recognition, in particular to a face feature extraction method based on Lp norm sample pair weighting. Background technique [0002] The amount of face image information is too large and the sample dimension is too high. Face recognition requires a lot of storage and calculation costs, resulting in the "curse of dimensionality". Therefore, it is necessary to process the face image before face recognition. Feature extraction can effectively process high-dimensional data, and use linear or nonlinear transformation to extract a small number of features that best represent the sample. Higher dimensions usually contain redundant features, and dimensionality reduction needs to ensure the integrity and inherent structure of the data as much as possible. [0003] PCA (Principal Component Analysis) is a traditional dimensionality reduction method, which can project high-dimensional data into a low...

AI Technical Summary

Problems solved by technology

However, the traditional PCA method uses the L2 norm in the objective function, so it is very sensitive to outliers or outliers

[0004] In order to solve this problem, in recent years, researchers have proposed a principal component analysis method (Lp-PCA-L1) that maximizes the L1 norm constrained by the Lp norm, which reduces the sensitivity to outliers, but it is easily affected by the sample mean. Unable to highlight the role of key features in the sample, it is difficult to adapt to data with complex features, affecting the recognition effect

Images