基于VGG-Face的人脸识别测试
VGG Face Descriptor 是牛津大学VGG小组的工作，现在已经开源训练好的网络结构和模型参数，本文将基于此模型在caffe上使用自己的人脸数据微调，并进行特征提取与精确度验证。
数据传送门：CASIA WebFace
模型传送门：http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
模型准备
1.从上面的网址中下载VGG-Face已经训练好的模型和网络结构文件，根据deploy.proto文件来修改得到train_val.prototxt文件，主要有：修改数据层的输入和最后全连接层以及损失层等，注意fc8层名称的修改，具体如下：
name: "VGG_FACE_16_layers"
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 224
# mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
}
data_param {
source: "vggface/webface_train_lmdb"
batch_size: 32
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 224
# mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
}
data_param {
source: "vggface/webface_val_lmdb"
batch_size: 32
backend: LMDB
}
}
......
layer {
bottom: "fc7"
top: "fc8_s"
name: "fc8_s"
type: "InnerProduct"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 2031
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8_s"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8_s"
bottom: "label"
top: "loss"
}模型训练
本次训练，我采用的是webface中人脸数量在50张以上的个人类别，总共有两千多个。按照caffe的工具转换成lmdb格式即可开始训练。
模型效果测试
模型训练结束之后，在lfw上验证实验效果。lfw数据对使用官方提供的txt文件，不过我觉得格式不太好，就自己用脚本进行了些许修改：
pairs.txt
Abel_Pacheco 1 4
Akhmed_Zakayev 1 3
Akhmed_Zakayev 2 3
Amber_Tamblyn 1 2
Anders_Fogh_Rasmussen 1 3
Anders_Fogh_Rasmussen 1 4修改后，将一对图片的路径放在一起，属于同一个人为1，否则为0
Abel_Pacheco/Abel_Pacheco_0001.jpg Abel_Pacheco/Abel_Pacheco_0004.jpg 1
Akhmed_Zakayev/Akhmed_Zakayev_0001.jpg Akhmed_Zakayev/Akhmed_Zakayev_0003.jpg 1
Akhmed_Zakayev/Akhmed_Zakayev_0002.jpg Akhmed_Zakayev/Akhmed_Zakayev_0003.jpg 1
Amber_Tamblyn/Amber_Tamblyn_0001.jpg Amber_Tamblyn/Amber_Tamblyn_0002.jpg 1脚本如下：
#!/usr/bin/python
#-*- coding: utf-8 -*-
'''
#Created on Thur Mar 2 10:17:38 2017
#Goal:parse pairs.txt of LFW database to label.txt for face #recognition test
#@author: wujiyang
'''
import sys
def get_all_images(filename):
file = open(filename)
lines = file.readlines()
list = []
for line in lines:
line_split = line.strip('\n').split('\t')
if(len(line_split)) == 3:
line_split[-1] = line_split[-1].zfill(4)
line_split[-2] = line_split[-2].zfill(4)
if(len(line_split)) == 4:
line_split[-1] = line_split[-1].zfill(4)
line_split[-3] = line_split[-3].zfill(4)
list.append(line_split)
file.close()
return list
def save2labelfile(list):
file = open('label.txt', 'w')
labellines = []
for i in range(len(list)):
if len(list[i]) == 3:
labelline = list[i][0] + '/' + list[i][0] + '_' + list[i][1] + '.jpg' + '\t' + 'original' + '/' + list[i][0] + '/' + list[i][0] + '_' +list[i][2] + '.jpg' + '\t' + '1\n'
labellines.append(labelline)
elif len(list[i]) == 4:
labelline = list[i][0] + '/' + list[i][0] + '_' + list[i][1] + '.jpg' + '\t' + 'original' + '/' + list[i][2] + '/' + list[i][2] + '_' + list[i][3] + '.jpg' + '\t' + '0\n'
labellines.append(labelline)
file.writelines(labellines)
file.close()
'''''
使用方法：执行脚本 python pair2label.py pairs.txt
'''
if __name__ == "__main__":
if len(sys.argv) != 2:
print "Format Error! Usuage: python %s pairs.txt" %(sys.argv[0])
sys.exit()
list = get_all_images("pairs.txt")
save2labelfile(list)
print "Done!"特征提取与精确度验证
此脚本采用caffe的python接口进行特征提取与验证。
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 20 16:55:55 2015
@author: wujiyang
@brief：在lfw数据库上验证训练好了的网络
"""
import math
import sklearn
import numpy as np
import matplotlib.pyplot as plt
import skimage
caffe_root = '/home/wujiyang/caffe/'
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
import sklearn.metrics.pairwise as pw
#模型初始化相关操作
def initilize():
print 'model initilizing...'
deployPrototxt = "./vgg-face-deploy.prototxt"
modelFile = "./vgg-face.caffemodel"
caffe.set_mode_gpu()
caffe.set_device(0)
net = caffe.Net(deployPrototxt, modelFile,caffe.TEST)
return net
def read_imagelist(labelfile):
'''
@brief：从列表文件中，读取图像数据到矩阵文件中
@param： labelfile 图像列表文件
@return ：4D 的矩阵
'''
file = open(labelfile)
lines = file.readlines()
test_num=len(lines)
file.close()
x = np.empty((test_num,3,224,224))
y = np.empty((test_num,3,224,224))
labels = []
i = 0
for line in lines:
path = line.strip('\n').split('\t')
#read left image
filename = path[0]
img = skimage.io.imread(filename,as_grey=False)
image = skimage.transform.resize(img,(224,224))*255
if image.ndim < 3:
print 'gray:'+filename
x[i,0,:,:]=image[:,:]
x[i,1,:,:]=image[:,:]
x[i,2,:,:]=image[:,:]
else:
x[i,0,:,:]=image[:,:,0]
x[i,1,:,:]=image[:,:,1]
x[i,2,:,:]=image[:,:,2]
#read right image
filename = path[1]
img = skimage.io.imread(filename,as_grey=False)
image = skimage.transform.resize(img,(224,224))*255
if image.ndim < 3:
print 'gray:'+filename
y[i,0,:,:]=image[:,:]
y[i,1,:,:]=image[:,:]
y[i,2,:,:]=image[:,:]
else:
y[i,0,:,:]=image[:,:,0]
y[i,1,:,:]=image[:,:,1]
y[i,2,:,:]=image[:,:,2]
#read label
labels.append(int(path[2]))
i=i+1
return x, y, labels
def extractFeature(leftdata,rightdata):
#提取左半部分的特征
test_num=np.shape(leftdata)[0]
#data 是输入层的名字
out = net.forward_all(data = leftdata)
feature1 = np.float64(out['fc7'])
featureleft=np.reshape(feature1,(test_num,4096))
#np.savetxt('feature1.txt', feature1, delimiter=',')
#提取右半部分的特征
out = net.forward_all(data = rightdata)
feature2 = np.float64(out['fc7'])
featureright=np.reshape(feature2,(test_num,4096))
#np.savetxt('feature2.txt', feature2, delimiter=',')
return featureleft, featureright
def calculate_accuracy(distance,labels,num):
'''
#计算识别率,
选取阈值，计算识别率
'''
accuracy = {}
predict = np.empty((num,))
threshold = 0.1
while threshold <= 0.9 :
for i in range(num):
if distance[i] >= threshold:
predict[i] = 0
else:
predict[i] = 1
predict_right = 0.0
for i in range(num):
if predict[i] == labels[i]:
predict_right += 1.0
current_accuracy = (predict_right / num)
accuracy[str(threshold)] = current_accuracy
threshold = threshold + 0.001
#将字典按照value排序
temp = sorted(accuracy.items(), key = lambda d:d[1], reverse = True)
highestAccuracy = temp[0][1]
thres = temp[0][0]
return highestAccuracy, thres
if __name__=='__main__':
#模型初始化
net = initilize()
print 'network input :' ,net.inputs
print 'network output： ', net.outputs
#读取图像数据
leftdata,rightdata,labels = read_imagelist('tmp.txt')
#计算特征
featureleft, featureright = extractFeature(leftdata, rightdata)
#计算每个特征之间的距离 cosine距离
test_num = len(labels)
mt = pw.pairwise_distances(featureleft, featureright, metric='cosine')
distance = np.empty((test_num,))
for i in range(test_num):
distance[i] = mt[i][i]
print 'Distance before normalization:\n', distance
print 'Distance max:', np.max(distance), ' Distance min:', np.min(distance), '\n'
# 距离需要归一化到0--1,与标签0-1匹配
distance_norm = np.empty((test_num,))
for i in range(test_num):
distance_norm[i] = (distance[i]-np.min(distance))/(np.max(distance)-np.min(distance))
print 'Distance after normalization:\n', distance_norm
print 'Distance_norm max:', np.max(distance_norm), ' Distance_norm min:', np.min(distance_norm), '\n'
#根据label和distance_norm计算精确度
highestAccuracy, threshold = calculate_accuracy(distance_norm,labels,len(labels))
print ("the highest accuracy is : %.4f, and the corresponding threshold is %s \n"%(highestAccuracy, threshold))