人脸识别---利用caffe实现多层特征学习人脸识别网络

摘要：本文主要讲解如何利用caffe搭建自己的网络，本文主要讲利用caffe搭建一种Hierarchical Feature Representation的网络。网络如下图：



数据库：CASIA-WebFace数据集，可以到我的网盘中下载:http://pan.baidu.com/s/1nuWsju5。在我的试验中我选用1000人的样本训练，在CASIA-WebFace数据中1000人包含图片最多，大概加起来有10W+的数据。

参看上篇博文：http://blog.csdn.net/hlx371240/article/details/51388022

1. 建立训练集和验证集

配置好ourfaceNet中的路径，执行ourfaceNet可以得到两个文件夹face_train_lmdb和face_val_lmdbface_val_lmdb.

ourfaceNet.sh

#!/usr/bin/env sh
# Create the imagenet lmdb inputs
# N.B. set the path to the imagenet train + val data dirs

EXAMPLE=facenet
DATA=facenet
TOOLS=./build/tools

TRAIN_DATA_ROOT=ourfacenet/data/
VAL_DATA_ROOT=ourfacenet/data/

# Set RESIZE=true to resize the images to 256x256. Leave as false if images have
# already been resized using another tool.
RESIZE=true
if $RESIZE; then
RESIZE_HEIGHT=128
RESIZE_WIDTH=128
else
RESIZE_HEIGHT=0
RESIZE_WIDTH=0
fi

if [ ! -d "$TRAIN_DATA_ROOT" ]; then
echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT"
echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \
"where the ImageNet training data is stored."
exit 1
fi

if [ ! -d "$VAL_DATA_ROOT" ]; then
echo "Error: VAL_DATA_ROOT is not a path to a directory: $VAL_DATA_ROOT"
echo "Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path" \
"where the ImageNet validation data is stored."
exit 1
fi

echo "Creating train lmdb..."

GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
--resize_height=$RESIZE_HEIGHT \
--resize_width=$RESIZE_WIDTH \
--shuffle \
$TRAIN_DATA_ROOT \
$DATA/train.txt \
$EXAMPLE/face_train_lmdb1

echo "Creating val lmdb..."

GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
--resize_height=$RESIZE_HEIGHT \
--resize_width=$RESIZE_WIDTH \
--shuffle \
$VAL_DATA_ROOT \
$DATA/val.txt \
$EXAMPLE/face_val_lmdb1

echo "Done."

求均值文件

make_mean.sh

EXAMPLE=facenet
DATA=facenet
TOOLS=./build/tools

$TOOLS/compute_image_mean $EXAMPLE/face_train_lmdb \
$DATA/face_mean.binaryproto

echo "Done."

这样可以得到face_mean.binaryproto

3.根据上图搭建自己的框架

我搭建的是分层式的网络，可以得到卷积层具有区分性的特征

train_val.prototxt

name: "train_val.prototxt"
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 128
mean_file: "facenet/face_mean.binaryproto"
}
data_param {
source: "facenet/face_train_lmdb"
batch_size: 50
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 128
mean_file: "facenet/face_mean.binaryproto"
}
data_param {
source: "facenet/face_val_lmdb"
batch_size: 50
backend: LMDB
}
}
layer {
name: "conv1/7x7_s2"
type: "Convolution"
bottom: "data"
top: "conv1/7x7_s2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 40
pad: 3
kernel_size: 7
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}

layer {
name: "conv1/relu_7x7"
type: "ReLU"
bottom: "conv1/7x7_s2"
top: "conv1/7x7_s2"
}

layer {
name: "pool1/3x3_s2"
type: "Pooling"
bottom: "conv1/7x7_s2"
top: "pool1/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "pool1/norm1"
type: "LRN"
bottom: "pool1/3x3_s2"
top: "pool1/norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}

layer {
name: "conv2/7x7_s2"
type: "Convolution"
bottom: "pool1/norm1"
top: "conv2/7x7_s2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 40
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}

layer {
name: "conv2/relu_7x7"
type: "ReLU"
bottom: "conv2/7x7_s2"
top: "conv2/7x7_s2"
}

layer {
name: "pool2/3x3_s2"
type: "Pooling"
bottom: "conv2/7x7_s2"
top: "pool2/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer {
name: "loss1/classifier"
type: "InnerProduct"
bottom: "pool2/3x3_s2"
top: "loss1/classifier"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss1/loss"
type: "SoftmaxWithLoss"
bottom: "loss1/classifier"
bottom: "label"
top: "loss1/loss1"
loss_weight: 0.3
}
layer {
name: "loss1/top-1"
type: "Accuracy"
bottom: "loss1/classifier"
bottom: "label"
top: "loss1/top-1"
include {
phase: TEST
}
}

layer {
name: "loss2/conv"
type: "Convolution"
bottom: "pool1/norm1"
top: "loss2/conv"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 40
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "loss2/relu_conv"
type: "ReLU"
bottom: "loss2/conv"
top: "loss2/conv"
}

layer {
name: "loss2/pool"
type: "Pooling"
bottom: "loss2/conv"
top: "loss2/pool"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer {
name: "loss2/classifier"
type: "InnerProduct"
bottom: "loss2/pool"
top: "loss2/classifier"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}

layer {
name: "loss2/loss"
type: "SoftmaxWithLoss"
bottom: "loss2/classifier"
bottom: "label"
top: "loss2/loss1"
loss_weight: 0.3
}
layer {
name: "loss2/top-1"
type: "Accuracy"
bottom: "loss2/classifier"
bottom: "label"
top: "loss2/top-1"
include {
phase: TEST
}
}

layer {
name: "loss3/conv"
type: "Convolution"
bottom: "loss2/pool"
top: "loss3/conv"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 40
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "loss3/relu_conv"
type: "ReLU"
bottom: "loss3/conv"
top: "loss3/conv"
}

layer {
name: "loss3/pool"
type: "Pooling"
bottom: "loss3/conv"
top: "loss3/pool"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer {
name: "loss3/classifier"
type: "InnerProduct"
bottom: "loss3/pool"
top: "loss3/classifier"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}

layer {
name: "loss3/loss"
type: "SoftmaxWithLoss"
bottom: "loss3/classifier"
bottom: "label"
top: "loss3/loss1"
loss_weight: 0.3
}
layer {
name: "loss3/top-1"
type: "Accuracy"
bottom: "loss3/classifier"
bottom: "label"
top: "loss3/top-1"
include {
phase: TEST
}
}

layer {
name: "loss4/conv"
type: "Convolution"
bottom: "loss3/pool"
top: "loss4/conv"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 40
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "loss4/relu_conv"
type: "ReLU"
bottom: "loss4/conv"
top: "loss4/conv"
}

layer {
name: "loss4/pool"
type: "Pooling"
bottom: "loss4/conv"
top: "loss4/pool"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer {
name: "loss4/fc"
type: "InnerProduct"
bottom: "loss4/pool"
top: "loss4/fc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "loss4/relu_fc"
type: "ReLU"
bottom: "loss4/fc"
top: "loss4/fc"
}
layer {
name: "loss4/drop_fc"
type: "Dropout"
bottom: "loss4/fc"
top: "loss4/fc"
dropout_param {
dropout_ratio: 0.7
}
}

layer {
name: "loss4/classifier"
type: "InnerProduct"
bottom: "loss4/fc"
top: "loss4/classifier"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}

layer {
name: "loss4/loss"
type: "SoftmaxWithLoss"
bottom: "loss4/classifier"
bottom: "label"
top: "loss4/loss1"
loss_weight: 1
}
layer {
name: "loss4/top-1"
type: "Accuracy"
bottom: "loss4/classifier"
bottom: "label"
top: "loss4/top-1"
include {
phase: TEST
}
}

4. 接下来就是写自己的solver.prototxt

solver1.prototxt

net: "facenet/train_val.prototxt"
test_iter: 1000
test_interval: 4000
test_initialization: false
display: 100
average_loss: 100
base_lr: 0.005
lr_policy: "step"
stepsize: 320000
gamma: 0.96
max_iter: 100000
momentum: 0.9
weight_decay: 0.0002
snapshot: 4000
snapshot_prefix: "facenet/face_model"
solver_mode: GPU

solver2.prototxt

net: "facenet/train_val.prototxt"
test_iter: 1000
test_interval: 4000
test_initialization: false
display: 100
average_loss: 100
base_lr: 0.001
lr_policy: "step"
stepsize: 320000
gamma: 0.96
max_iter: 100000
momentum: 0.9
weight_decay: 0.0002
snapshot: 4000
snapshot_prefix: "facenet/face_model"
solver_mode: GPU

solver3.prototxt

net: "facenet/train_val.prototxt"
test_iter: 1000
test_interval: 4000
test_initialization: false
display: 100
average_loss: 100
base_lr: 0.0001
lr_policy: "step"
stepsize: 320000
gamma: 0.96
max_iter: 100000
momentum: 0.9
weight_decay: 0.0002
snapshot: 4000
snapshot_prefix: "facenet/face_model"
solver_mode: GPU

包括了三个solver.prototxt，三个solver.prototxt有不同的学习率，都学习了10w次迭代。

5.建立自己的训练脚本

./build/tools/caffe train \
--solver=facenet/solver.prototxt \

./build/tools/caffe train \
--solver=facenet/solver.prototxt \
--weights=facenet/face_model_iter_100000.caffemodel -gpu=0

./build/tools/caffe train \
--solver=facenet/solver.prototxt \
--weights=facenet/face_model_iter_200000.caffemodel -gpu=0

最后执行各个脚本就可以把自己的网络搭建起来了。当然我们可以根据自己的需求去搭建我们的网络，我们只要记住name,top,bottom这三个就可以轻松搭建任意的网络去完成我们的任务。