The emotion-1 from fitrialif

emotion-1's Introduction

emotion recognition

predict emotion from video.

usage

git clone https://github.com/mynameisvinn/emotion
python emotion.py  # make sure your webcam is turned on

model

architecture

model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu',
                        input_shape=(1, X_train.shape[2], X_train.shape[3])))
model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu'))
model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(7, activation='softmax'))  # 7 classes

dataset

the dataset consists of 28,709 examples of 48x48 pixel grayscale images of faces, categorized as one of the following: angry, disgust, fear, happy, sad, surprise, neutral. for more information, visit fer.

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