Image latent semantic analysis based face recognition with ensemble extreme learning machine

Chao Wang, Ju Cheng Yang, Yarui Chen, Cao Wu, Yanbin Jiao
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引用次数: 1

Abstract

To overcome the shortcomings of the traditional methods, this paper proposes a novel face recognition method based on the image latent semantic features and ensemble extreme learning machine. The image latent semantic analysis is to acquire the high-level features from the face image, which has good robustness to illumination and expression changes. The image latent features are extracted as fellows: firstly, we obtain the low-level features of face image by the feature extraction methods of Gabor filter, local ternary pattern (LTP), and invariant moments. Then, we build the feature-image matrix based on the low-level features, and decompose the matrix with two dimension matrix decomposition to get the image latent semantic features. Finally, the ensemble extreme learning machine is used to classify the latent semantic features, which combines lots of extreme learning machine to obtain a stable classifier. The experimental results show that our proposed algorithm is more effective when compared with other algorithms.
基于图像潜在语义分析的集成极限学习机人脸识别
针对传统人脸识别方法的不足,提出了一种基于图像潜在语义特征和集成极限学习机的人脸识别方法。图像潜在语义分析是从人脸图像中获取高级特征,对光照和表情变化具有较好的鲁棒性。首先,通过Gabor滤波、局部三元模式(LTP)和不变矩的特征提取方法获得人脸图像的底层特征;然后,基于底层特征构建特征-图像矩阵,并用二维矩阵分解对矩阵进行分解,得到图像的潜在语义特征。最后,利用集成极值学习机对潜在语义特征进行分类,将多个极值学习机组合在一起,得到一个稳定的分类器。实验结果表明,与其他算法相比,本文提出的算法更加有效。
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