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Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
Raw
README.md

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

If you find our models useful, please add suitable reference to our paper in your work.

gist_id: 54aee1b8b0397721aa4b

Emotion Classification CNN - RGB

caffemodel: VGG_S_rgb/EmotiW_VGG_S.caffemodel

caffemodel_url: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_rgb/EmotiW_VGG_S.caffemodel

mean_file_proto: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_rgb/mean.binaryproto

Emotion Classification CNN - LBP

caffemodel: VGG_S_lbp/EmotiW_VGG_S.caffemodel

caffemodel_url: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_lbp/EmotiW_VGG_S.caffemodel

mean_file_proto: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_lbp/mean.binaryproto

Emotion Classification CNN - Cyclic LBP

caffemodel: VGG_S_cyclic_lbp/EmotiW_VGG_S.caffemodel

caffemodel_url: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_cyclic_lbp/EmotiW_VGG_S.caffemodel

mean_file_proto: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_cyclic_lbp/mean.binaryproto

Emotion Classification CNN - Cyclic LBP-5

caffemodel: VGG_S_cyclic_lbp_5/EmotiW_VGG_S.caffemodel

caffemodel_url: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_cyclic_lbp_5/EmotiW_VGG_S.caffemodel

mean_file_proto: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_cyclic_lbp_5/mean.binaryproto

Emotion Classification CNN - Cyclic LBP-10

caffemodel: VGG_S_cyclic_lbp_10/EmotiW_VGG_S.caffemodel

caffemodel_url: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_cyclic_lbp_10/EmotiW_VGG_S.caffemodel

mean_file_proto: https://dl.dropboxusercontent.com/u/38822310/demodir/VGG_S_cyclic_lbp_10/mean.binaryproto

Copyright 2015, Gil Levi and Tal Hassner

The SOFTWARE provided in this page is provided "as is", without any guarantee made as to its suitability or fitness for any particular use. It may contain bugs, so use of this tool is at your own risk. We take no responsibility for any damage of any sort that may unintentionally be caused through its use.

Raw
deploy.txt
name: "CaffeNet"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
convolution_param {
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kernel_size: 7
stride: 2
}
}
layers {
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type: RELU
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top: "conv1"
}
layers {
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type: LRN
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top: "norm1"
lrn_param {
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alpha: 0.0005
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}
}
layers {
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type: POOLING
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 3
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "pool1"
top: "conv2"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "pool2"
top: "conv3"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
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type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "conv4"
type: CONVOLUTION
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top: "conv4"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
name: "relu4"
type: RELU
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top: "conv4"
}
layers {
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type: CONVOLUTION
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convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
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top: "conv5"
}
layers {
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bottom: "conv5"
top: "pool5"
pooling_param {
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kernel_size: 3
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}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5"
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inner_product_param {
num_output: 4048
}
}
layers {
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type: RELU
bottom: "fc6"
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}
layers {
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bottom: "fc6"
top: "fc6"
dropout_param {
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}
}
layers {
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bottom: "fc6"
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inner_product_param {
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}
layers {
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}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
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layers {
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type: INNER_PRODUCT
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inner_product_param {
num_output: 7
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layers {
name: "prob"
type: SOFTMAX
bottom: "fc8"
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}
Raw
train_val.prototxt
name: "CaffeNet"
layers {
name: "data"
type: DATA
top: "data"
top: "label"
data_param {
source: "/home/ubuntu/EmotiW/lmdb/train_lmdb"
backend: LMDB
batch_size: 30
}
transform_param {
crop_size: 224
mean_file: "/home/ubuntu/EmotiW/mean_image/mean.binaryproto"
mirror: true
}
include: { phase: TRAIN }
}
layers {
name: "data"
type: DATA
top: "data"
top: "label"
data_param {
source: "/home/ubuntu/EmotiW/lmdb/val_lmdb"
backend: LMDB
batch_size: 20
}
transform_param {
crop_size: 224
mean_file: "/home/ubuntu/EmotiW/mean_image/mean.binaryproto"
mirror: false
}
include: { phase: TEST }
}
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 7
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "norm1"
type: LRN
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0005
beta: 0.75
}
}
layers {
name: "pool1"
type: POOLING
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 3
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "pool1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
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type: CONVOLUTION
bottom: "pool2"
top: "conv3"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
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pad: 1
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weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
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type: CONVOLUTION
bottom: "conv3"
top: "conv4"
blobs_lr: 1
blobs_lr: 2
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weight_decay: 0
convolution_param {
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pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
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}
}
}
layers {
name: "relu4"
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top: "conv4"
}
layers {
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type: CONVOLUTION
bottom: "conv4"
top: "conv5"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu5"
type: RELU
bottom: "conv5"
top: "conv5"
}
layers {
name: "pool5"
type: POOLING
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 3
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5"
top: "fc6"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4048
weight_filler {
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std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu6"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.7
}
}
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "fc6"
top: "fc7"
blobs_lr: 9
blobs_lr: 18
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4048
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu7"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.7
}
}
layers {
name: "fc8_cat"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
blobs_lr: 12
blobs_lr: 24
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 7
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "accuracy"
type: ACCURACY
bottom: "fc8"
bottom: "label"
top: "accuracy"
include: { phase: TEST }
}
layers {
name: "loss"
type: SOFTMAX_LOSS
bottom: "fc8"
bottom: "label"
top: "loss"
}
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