基于改进协作表示的人脸表情分类  

Facial Expression Classification Based on Improved Collaborative Representation

作　　者：李莉[1] 穆佳玮 LI Li;MU Jia-Wei(School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学计算机科学与通信工程学院,镇江212013

出　　处：《计算机系统应用》2021年第5期196-201,共6页Computer Systems & Applications

摘　　要：如今,人脸表情的相关研究是非常热门的话题,研究者愈发的关注其相关分类算法.而提高分类的精度对人工智能等相关前沿领域具有实际的应用价值.目前图像分类的方法层见叠出,其中较为经典的有线性判别分析和稀疏表示等.针对图像分类计算复杂度高,特征利用率以及分类精度等相关问题,本文提出了一种改进的协作表示分类算法.首先采用分块加权局部二值模式对图像进行分块处理,得到每个子块的纹理特征向量,为了避免维度灾难使用主成分分析法进行降维,同时也能够提升算法的运行速度,最后采用竞争协作表示分类算法得到最终的分类结果.通过实验及结果分析对比表明,特征提取和协作表示算法的结合有着较好的分类效果.Nowadays,researchers are paying special attention to the classification algorithms related to facial expression,and improving the accuracy of classification is of practical value to frontier fields such as artificial intelligence.The classic methods for image classification are linear discriminant analysis and sparse representation.This study proposes an improved collaborative representation algorithm,aiming at the high computational complexity of image classification,feature utilization,and classification accuracy.First,the block weighted local binary patterns are applied to the texture feature vector of each sub-block.Then,principal component analysis is used to avoid the curse of dimensionality and also increase the running speed of the proposed algorithm.Finally,a collaborative-competitive representation algorithm is adopted to obtain the final classification results.In conclusion,the combination of feature extraction with collaborative representation algorithms has a good classification effect.

关 键 词：表情分类 协作表示 局部二值模式 特征提取 竞争协作表示 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]