基于改进的有监督无参局部保持投影算法的人脸识别

doi:10.6040/j.issn.1672-3961.0.2017.419

山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (1): 10-16.doi: 10.6040/j.issn.1672-3961.0.2017.419

基于改进的有监督无参局部保持投影算法的人脸识别

范君^1,²(),业巧林¹,业宁^1,*()

1. 南京林业大学信息科学技术学院, 江苏南京 210037
2. 江苏工程职业技术学院建筑工程学院, 江苏南通 226007

收稿日期:2017-08-24 出版日期:2019-02-20 发布日期:2019-03-01
通讯作者: 业宁 E-mail:13773679049@139.com;njfuyn@163.com
作者简介:范君(1975—)，男，江苏南通人，副教授，硕士，主要研究方向为模式识别，机器学习. E-mail:13773679049@139.com
基金资助:
江苏省高等职业院校国内高级访问学者计划(2016GRFX013);江苏省青蓝工程培养对象(2016-15);江苏省住建厅建设系统科技计划(2017ZD138);江苏省高校哲学社会科学研究(2018SJA1247);校科研计划(GYKY/2017/5, GYKY/2017/12)

Face recognition based on improved prameter-free supervised localitypreserving projections

Jun FAN^1,²(),Qiaolin YE¹,Ning YE^1,*()

1. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
2. School of Civil Engineering, Jiangsu College of Engineering and Technology, Nantong 226007, Jiangsu, China

Received:2017-08-24 Online:2019-02-20 Published:2019-03-01
Contact: Ning YE E-mail:13773679049@139.com;njfuyn@163.com
Supported by:
江苏省高等职业院校国内高级访问学者计划(2016GRFX013);江苏省青蓝工程培养对象(2016-15);江苏省住建厅建设系统科技计划(2017ZD138);江苏省高校哲学社会科学研究(2018SJA1247);校科研计划(GYKY/2017/5, GYKY/2017/12)

摘要/Abstract

摘要：

针对局部保持投影算法的无监督性质和参数选择复杂性问题,提出一种改进的有监督无参数局部保持投影算法(supervised dice parameter-free locality preserving projection, SdPLPP)。SdPLPP算法使用广义Dice系数构建近邻矩阵,有效避免局部保持投影(locality preserving projection algorithm, LPP)算法参数选择调整的问题,采用监督模式对数据进行特征提取。SdPLPP在Iris数据集进行了图像可视化试验,直观分析试验分类后的样本距离值与算法性能的关系,并在ORL, Yale, FERET 3种人脸库上进行试验,通过对人脸数据的特征提取,采用最近邻分类法统计识别率,验证SdPLPP算法的有效性。试验结果表明:在人脸识别率方面, SdPLPP算法优于PCA, ULDA, LPP, SPLPP和EP-SLPP的算法,并优于已提出的其他有监督无参数局部保持投影算法。

关键词: 特征提取, 局部保持投影, 无参数近邻矩阵, 广义Dice系数, 人脸识别

Abstract:

To solve the problem of unsupervised and complexity of parameter selection of the locality preserving projection algorithm, an improved supervised dice parameter-free locality preserving projection algorithm (SdPLPP)was proposed. SdPLPP constructed affinity matrix by using generalized Dice coefficient and extract features of data under the supervised mode, which could avoid the problems of parameters selection and adjustment of locality preserving projection (LPP) algorithm. The proposed algorithm performed experiment of image visualization based on the Iris dataset, analyzed the relationship between the value of the distance of sample data and the performance of the algorithm. To verifying the effectiveness and performance of algorithm, SdPLPP carried out the feature extraction experiments based on three kinds of human face databases, such as ORL, Yale and FERET, and used nearest neighbor classifier to get correct recognition rate. The experimental results showed that the SdPLPP algorithm was superior to PCA, ULDA, LPP, SPLPP and EP-SLPP algorithm in face recognition, and it was better than other algorithms of supervised parameter-free locality preserving projections.

Key words: feature extraction, locality preserving projections (LPP), parameter-free affninty matrix, generalized Dice coefficient, face recognition

中图分类号:

TP391.4

范君,业巧林,业宁. 基于改进的有监督无参局部保持投影算法的人脸识别[J]. 山东大学学报 (工学版), 2019, 49(1): 10-16.

Jun FAN,Qiaolin YE,Ning YE. Face recognition based on improved prameter-free supervised localitypreserving projections[J]. Journal of Shandong University(Engineering Science), 2019, 49(1): 10-16.

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cm
算法	M₁	M₂	M₃	S_w23	S_b23	r₂₃
PCA	0.54	0.77	0.92	0.84	1.56	1.84
ULDA	0.14	0.13	0.17	0.15	0.17	1.13
LPP	0.49	0.74	0.88	0.81	1.64	2.03
SPLPP	0.39	0.39	0.44	0.41	0.80	1.93
EP-SLPP	0.35	0.35	0.45	0.40	0.94	2.35
SdPLPP	0.32	0.59	0.68	0.63	1.64	2.58

%
算法	训练集个数
算法	2	3	4	5
PCA	68.40	78.25	83.13	87.90
ULDA	79.47	86.46	91.08	93.95
LPP	67.53	77.43	82.25	87.05
SPLPP	71.44	81.36	85.08	89.30
EP-SLPP	79.50	86.46	91.25	94.05
SdPLPP	83.66	91.07	94.33	96.50

%
算法	训练集个数
算法	2	3	4	5
PCA	42.15	48.83	52. 67	56.11
ULDA	56.00	66.83	74.57	77.44
LPP	41.78	49.25	52.29	55.11
SPLPP	42.96	50.25	53.14	56.89
EP-SLPP	54.52	66.75	74.86	78.78
SdPLPP	55.04	66.92	75.90	80.11

训练集	Test1	Test2	Test3	Test4
组合方式	Ia+Ib+Id	Ia+Ib+Ie	Ia+Ic+Id	Ia+Ic+Ie

%
算法	训练集
算法	Test1	Test2	Test3	Test4
PCA	78.25	81.75	70.75	73.50
ULDA	78.17	85.83	74.67	83.67
LPP	78.75	81.75	71.00	74.00
SPLPP	78.75	82.00	70.75	74.00
EP-SLPP	88.33	92.33	80.67	89.17
SdPLPP	86.50	90.67	82.50	89.67

基于改进的有监督无参局部保持投影算法的人脸识别

Face recognition based on improved prameter-free supervised localitypreserving projections

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