Face Recognition Using Fuzzy Clustering and Kernel Least Square
Face Recognition Using Fuzzy Clustering and Kernel Least Square
复制标题
使用模糊聚类和核最小二乘进行人脸识别
DOI：
10.4236/jcc.2015.33001
复制
发表时间：
2015
影响因子：
3
通讯作者：
E. A. Daoud
中科院分区：
文献类型：
--
作者：
E. A. Daoud
在过去的十五年里，人脸识别已经成为图像分析的热门研究领域，也是机器学习和理解最成功的应用之一。为了提高图像识别的分类率，引入、修改和组合了几种技术。该模型采用Fourier-Gabor滤波器提取特征，利用信噪比选择最佳特征，利用模糊c均值聚类删除或修改异常图像，利用核最小二乘法进行核最小二乘法，并利用野狗包优化算法进行优化。为了将所提出的方法与以前的方法进行比较，使用了四个数据集。结果表明，不带模糊聚类和带模糊聚类的建议方法在所有数据集上都优于最先进的方法。
Over the last fifteen years, face recognition has become a popular area of research in image analysis and one of the most successful applications of machine learning and understanding. To enhance the classification rate of the image recognition, several techniques are introduced, modified and combined. The suggested model extracts the features using Fourier-Gabor filter, selects the best features using signal to noise ratio, deletes or modifies anomalous images using fuzzy c-mean clustering, uses kernel least square and optimizes it by using wild dog pack optimization. To compare the suggested method with the previous methods, four datasets are used. The results indicate that the suggested methods without fuzzy clustering and with fuzzy clustering outperform state- of-art methods for all datasets.