SpPCANet: a simple deep learning-based feature extraction approach for 3D face recognition
SpPCANet: a simple deep learning-based feature extraction approach for 3D face recognition
复制标题
DOI：
10.1007/s11042-020-09554-6
复制
发表时间：
2020-08-20
影响因子：
3.6
通讯作者：
Nasipuri, Mita
中科院分区：
文献类型：
--
作者：
Dutta, Koushik;Bhattacharjee, Debotosh;Nasipuri, Mita
提出了一种基于稀疏主成分分析网络(SpPCANet)的三维人脸识别方法。该网络由三个基本部分组成：(1)多级稀疏主成分分析滤波器；(2)二进制散列；(3)分块直方图计算。这里，在卷积阶段使用稀疏主成分分析学习多级滤波器组，然后使用二进制散列进行索引，并使用分块直方图进行池化。最后，利用线性支持向量机对SPPCANet提取的特征进行分类。提出的网络SpPCANet是一个轻量级的深度学习网络。三个著名的三维人脸数据库，即Frav3D，Bsporus3D和Casia3D，被用来验证所提出的系统。通过改变不同的参数，例如卷积层的滤波器数和卷积层的滤波器的大小以及池层的非重叠块的大小，已经对该网络进行了广泛的研究。在处理Frav3D、Bsporus3D和Casia3D数据库中提供的所有类型的人脸变异时，系统分别获得了96.93%、98.54%和88.80%的识别率。
A Sparse Principal Component Analysis Network (SpPCANet) based feature extraction is proposed here for 3D face recognition. The network consists of three basic components: (1) Multistage sparse principal component analysis filters, (2) Binary hashing, and (3) Block-wise histogram computation. Here, the sparse principal component analysis is used to learn multistage filter banks at the convolution stage, which is followed by binary hashing for indexing and block-wise histogram for pooling. Finally, a linear support vector machine (SVM) is used for classifying the features extracted by SpPCANet. The proposed network SpPCANet is a lightweight deep learning network. Three well-known 3D face databases, namely, Frav3D, Bosphorus3D, and Casia3D, are used for validating the proposed system. This proposed network has been extensively studied by varying different parameters, such as the number of filters at the convolution layer and the size of filters at the convolution layer and size of non-overlapping blocks at the pooling layer. Handling all types of variation of faces available in Frav3D, Bosphorus3D, and Casia3D databases, the system has acquired 96.93%, 98.54%, and 88.80% recognition rates, respectively.