====人脸识别FACE====
=====功能实现示例=====
#训练集（From Bing Picture）
#测试集（From Bing Picture）
#效果
=====代码解读与实现=====
FACE 主要有 FACEHandler 和 FACEServer 两部分代码组成。
先看一下CONFIG的结构
两个卷积层的卷积神经网络（CNN），C++实现
#第一层：输入层
name: "face"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 152
input_dim: 152
#第二层：卷积层
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
convolution_param {
num_output: 32
kernel_size: 11
stride: 1
}
}
#第三层：池层
layers {
name: "pool2"
type: POOLING
bottom: "conv1"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
#第四层：卷积层
layers {
name: "conv3"
type: CONVOLUTION
bottom: "pool2"
top: "conv3"
convolution_param {
num_output: 16
kernel_size: 9
stride: 1
}
}
#第五层：LOCAL
layers {
name: "local4"
type: LOCAL
bottom: "conv3"
top: "local4"
local_param {
num_output: 16
kernel_size: 9
stride: 1
}
}
#第六层：LOCAL
layers {
name: "local5"
type: LOCAL
bottom: "local4"
top: "local5"
local_param {
num_output: 16
kernel_size: 7
stride: 2
}
}
#第七层：LOCAL
layers {
name: "local6"
type: LOCAL
bottom: "local5"
top: "local6"
local_param {
num_output: 16
kernel_size: 5
stride: 1
}
}
#第八层：内积
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "local6"
top: "fc7"
inner_product_param {
num_output: 4096
}
}
#第九层：内积
layers {
name: "fc8"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
inner_product_param {
num_output: 83
}
}
#第十层：SOFTMAX
layers {
name: "prob"
type: SOFTMAX
bottom: "fc8"
top: "prob"
}
#第十一层：ARGMAX
layers {
name: "argmax"
type: ARGMAX
bottom: "prob"
top: "argmax"
}
=====小结=====
IMC主要运用了深度学习框架以及健康且强大的Folly Futures库，对图像进行预先处理，并最终有效的实现图像的分类及识别。此项功能巧妙运用了Intelligent Personal Assistants, 结合人声与图像，为未来虚拟助手的发展，提供了平台及机遇。
====语音识别ASR====