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利用Python sklearn的SVM对AT&T人脸数据进行人脸识别

要求:使用10-fold交叉验证方法实现SVM的对人脸库识别,列出不同核函数参数对识别结果的影响,要求画对比曲线。

使用Python完成,主要参考文献【4】,其中遇到不懂的功能函数一个一个的查官方文档和相关资料。其中包含了使用Python画图,遍历文件,读取图片,PCA降维,SVM,交叉验证等知识。

0.数据说明预处理

下载AT&T人脸数据(http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html),解压缩后为40个文件夹,每个文件夹是一个人的10张人脸照片。使用Python的glob库和PIL的Image读取照片,并转化为一维向量。这里需要注意,glob并非按照顺序读取,所以需要按照文件夹一个人一个人的读取数据,并标记对应分类。

 1 PICTURE_PATH = u"F:\\att_faces"
 2 
 3 all_data_set = [] #原始总数据集,二维矩阵n*m,n个样例,m个属性
 4 all_data_label = [] #总数据对应的类标签
 5 def get_picture():
 6     label = 1
 7     #读取所有图片并一维化
 8     while (label <= 20):
 9         for name in glob.glob(PICTURE_PATH + "\\s" + str(label) + "\\*.pgm"):
10             img = Image.open(name)
11             #img.getdata()
12             #np.array(img).reshape(1, 92*112)
13             all_data_set.append( list(img.getdata()) )
14             all_data_label.append(label)
15         label += 1
16 
17 get_picture()

 

1.PCA降维

获得原始数据后,对数据使用PCA降维处理,其中设定降维后的特征数目时遇到了问题,参考资料中n_components设定为150,但是该数据集采用大的该值后识别率会非常低,即虽然可以百分百识别出训练集人脸,但无法预测识别出新的脸,发生了过拟合(?)。经过把参数n_components设置为16后,产生了非常好的结果。PCA降维后数据的维数取多少比较好?有什么标准判断?注意,若维数较高,SVM训练会非常慢并且占用很高内存,维数小反而取得了很好的结果和效率。

另外,例子中是分别对测试集与训练集使用PCA降维,即PCA fit时只用了训练集。将数据转换为numpy的array类型是为了后面编程方便。

1 n_components = 16#这个降维后的特征值个数如果太大,比如100,结果将极其不准确,为何??
2 pca = PCA(n_components = n_components, svd_solver=auto, 
3           whiten=True).fit(all_data_set)
4 #PCA降维后的总数据集
5 all_data_pca = pca.transform(all_data_set)
6 #X为降维后的数据,y是对应类标签
7 X = np.array(all_data_pca)
8 y = np.array(all_data_label)

 

 

2. SVM训练与识别

对降维后的数据进行训练与识别。

 

 1 #输入核函数名称和参数gamma值,返回SVM训练十折交叉验证的准确率
 2 def SVM(kernel_name, param):
 3     #十折交叉验证计算出平均准确率
 4     #n_splits交叉验证,随机取
 5     kf = KFold(n_splits=10, shuffle = True)
 6     precision_average = 0.0
 7     param_grid = {C: [1e3, 5e3, 1e4, 5e4, 1e5]}#自动穷举出最优的C参数
 8     clf = GridSearchCV(SVC(kernel=kernel_name, class_weight=balanced, gamma = param),
 9                        param_grid)
10     for train, test in kf.split(X):
11         clf = clf.fit(X[train], y[train])
12         #print(clf.best_estimator_)
13         test_pred = clf.predict(X[test])
14         #print classification_report(y[test], test_pred)
15         #计算平均准确率
16         precision = 0
17         for i in range(0, len(y[test])):
18             if (y[test][i] == test_pred[i]):
19                 precision = precision + 1
20         precision_average = precision_average + float(precision)/len(y[test])
21     precision_average = precision_average / 10    
22     #print (u"准确率为" + str(precision_average))
23     return precision_average

 

 

3.主程序,设置不同参数对比分析

根据例子中的gamma值选择,发现其可以从非常小开始,即0.0001,经过人工实验,到1时rbf kernel出现了较差的结果,所以画图对比时在0.0001至1之间取100个点,因为点多后程序运行会非常慢。程序中的x_label即枚举的gamma参数值。为了节省时间,数据只选择了前20个人,最终执行时间为366.672秒。

 1 t0 = time()    
 2 kernel_to_test = [rbf, poly, sigmoid]
 3 #rint SVM(kernel_to_test[0], 0.1)
 4 plt.figure(1)
 5 
 6 for kernel_name in kernel_to_test:
 7     x_label = np.linspace(0.0001, 1, 100)
 8     y_label = []
 9     for i in x_label:
10         y_label.append(SVM(kernel_name, i))
11     plt.plot(x_label, y_label, label=kernel_name)
12     
13          
14 print("done in %0.3fs" % (time() - t0))    
15 plt.xlabel("Gamma")
16 plt.ylabel("Precision")
17 plt.title(Different Kernels Contrust) 
18 plt.legend()
19 plt.show()    
20     
21     

 

 

技术分享 

Figure 1 不同核函数不同参数识别率对比图

 

参考:

[1] Philipp Wagner.Face Recognition with Python. July 18, 2012

[2] Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. A Practical Guide to Support Vector Classication. National Taiwan University, Taipei 106, Taiwan.

[3] http://www.cnblogs.com/cvlabs/archive/2010/04/13/1711470.html

[4]http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html#sphx-glr-auto-examples-applications-face-recognition-py

[5] http://blog.csdn.net/ikerpeng/article/details/20370041

 

附录完整代码:

技术分享
  1 # -*- coding: utf-8 -*-
  2 """
  3 Created on Fri Dec 02 15:51:14 2016
  4 
  5 @author: JiaY
  6 """
  7 from time import time
  8 from PIL import Image
  9 import glob
 10 import numpy as np
 11 import sys
 12 from sklearn.model_selection import KFold
 13 from sklearn.model_selection import train_test_split
 14 from sklearn.decomposition import PCA
 15 from sklearn.model_selection import GridSearchCV
 16 from sklearn.svm import SVC
 17 from sklearn.metrics import classification_report
 18 import matplotlib.pyplot as plt
 19 
 20 #设置解释器为utf8编码,不知为何文件开头的注释没用。
 21 #尽管这样设置,在IPython下仍然会出错,只能用原装Python解释器执行本程序
 22 reload(sys)
 23 sys.setdefaultencoding("utf8")
 24 print sys.getdefaultencoding()
 25 
 26 PICTURE_PATH = u"F:\\课程相关资料\\研究生——数据挖掘\\16年作业\\att_faces"
 27 
 28 all_data_set = [] #原始总数据集,二维矩阵n*m,n个样例,m个属性
 29 all_data_label = [] #总数据对应的类标签
 30 def get_picture():
 31     label = 1
 32     #读取所有图片并一维化
 33     while (label <= 20):
 34         for name in glob.glob(PICTURE_PATH + "\\s" + str(label) + "\\*.pgm"):
 35             img = Image.open(name)
 36             #img.getdata()
 37             #np.array(img).reshape(1, 92*112)
 38             all_data_set.append( list(img.getdata()) )
 39             all_data_label.append(label)
 40         label += 1
 41 
 42 get_picture()
 43 
 44 n_components = 16#这个降维后的特征值个数如果太大,比如100,结果将极其不准确,为何??
 45 pca = PCA(n_components = n_components, svd_solver=auto, 
 46           whiten=True).fit(all_data_set)
 47 #PCA降维后的总数据集
 48 all_data_pca = pca.transform(all_data_set)
 49 #X为降维后的数据,y是对应类标签
 50 X = np.array(all_data_pca)
 51 y = np.array(all_data_label)
 52 
 53 
 54 #输入核函数名称和参数gamma值,返回SVM训练十折交叉验证的准确率
 55 def SVM(kernel_name, param):
 56     #十折交叉验证计算出平均准确率
 57     #n_splits交叉验证,随机取
 58     kf = KFold(n_splits=10, shuffle = True)
 59     precision_average = 0.0
 60     param_grid = {C: [1e3, 5e3, 1e4, 5e4, 1e5]}#自动穷举出最优的C参数
 61     clf = GridSearchCV(SVC(kernel=kernel_name, class_weight=balanced, gamma = param),
 62                        param_grid)
 63     for train, test in kf.split(X):
 64         clf = clf.fit(X[train], y[train])
 65         #print(clf.best_estimator_)
 66         test_pred = clf.predict(X[test])
 67         #print classification_report(y[test], test_pred)
 68         #计算平均准确率
 69         precision = 0
 70         for i in range(0, len(y[test])):
 71             if (y[test][i] == test_pred[i]):
 72                 precision = precision + 1
 73         precision_average = precision_average + float(precision)/len(y[test])
 74     precision_average = precision_average / 10    
 75     #print (u"准确率为" + str(precision_average))
 76     return precision_average
 77 
 78 t0 = time()    
 79 kernel_to_test = [rbf, poly, sigmoid]
 80 #rint SVM(kernel_to_test[0], 0.1)
 81 plt.figure(1)
 82 
 83 for kernel_name in kernel_to_test:
 84     x_label = np.linspace(0.0001, 1, 100)
 85     y_label = []
 86     for i in x_label:
 87         y_label.append(SVM(kernel_name, i))
 88     plt.plot(x_label, y_label, label=kernel_name)
 89     
 90          
 91 print("done in %0.3fs" % (time() - t0))    
 92 plt.xlabel("Gamma")
 93 plt.ylabel("Precision")
 94 plt.title(Different Kernels Contrust) 
 95 plt.legend()
 96 plt.show()    
 97     
 98     
 99     
100 """
101 clf = GridSearchCV(SVC(kernel=‘rbf‘, class_weight=‘balanced‘), param_grid)
102 X_train, X_test, y_train, y_test = train_test_split(
103     X, y, test_size=0.1, random_state=42)
104 clf = clf.fit(X_train, y_train)
105 test_pred = clf.predict(X_test)
106 print classification_report(y_test, test_pred)
107 
108 #十折交叉验证计算出平均准确率
109 precision_average = 0.0
110 for train, test in kf.split(X):
111     clf = clf.fit(X[train], y[train])
112     #print(clf.best_estimator_)
113     test_pred = clf.predict(X[test])
114     #print classification_report(y[test], test_pred)
115     #计算平均准确率
116     precision = 0
117     for i in range(0, len(y[test])):
118         if (y[test][i] == test_pred[i]):
119             precision = precision + 1
120     precision_average = precision_average + float(precision)/len(y[test])
121 precision_average = precision_average / 10    
122 print ("准确率为" + str(precision_average))
123 print("done in %0.3fs" % (time() - t0))
124 """
125 """               
126 print("Fitting the classifier to the training set")
127 t0 = time()
128 param_grid = {‘C‘: [1e3, 5e3, 1e4, 5e4, 1e5],
129               ‘gamma‘: [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
130 clf = GridSearchCV(SVC(kernel=‘rbf‘, class_weight=‘balanced‘), param_grid)
131 clf = clf.fit(all_data_pca, all_data_label)
132 print("done in %0.3fs" % (time() - t0))
133 print("Best estimator found by grid search:")
134 print(clf.best_estimator_)
135 all_data_set_pred = clf.predict(all_data_pca)
136 #target_names = range(1, 11)
137 print(classification_report(all_data_set_pred, all_data_label))
138 """
View Code

 

利用Python sklearn的SVM对AT&T人脸数据进行人脸识别