Very simple Emotion Recognition

In this article, I will try to provide the simplest TensorFlow Emotion Recognition implementation using TensorFlow.

GitHub: https://github.com/aruno14/emotionRecognition

First, the data

We will use FER2013 Dataset.

• https://www.kaggle.com/msambare/fer2013

Then, the code

We use MobileNetV2 Model in order to keep the model light and usable on small devices.

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
image_size = (48, 48)
batch_size = 32
epoch = 15
train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
    "emotions/train",
    target_size=image_size,
    color_mode="grayscale",
    shuffle=True,
    batch_size=batch_size,
    class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
    "emotions/test",
    target_size=image_size,
    shuffle=True,
    color_mode="grayscale",
    batch_size=batch_size,
    class_mode='categorical')
print(train_generator.class_indices)
classifier = tf.keras.applications.mobilenet_v2.MobileNetV2(include_top=True, weights=None, input_tensor=None, input_shape=image_size + (1,), pooling=None, classes=7)
classifier.compile(loss='categorical_crossentropy', metrics=['accuracy'])
classifier.fit(train_generator, steps_per_epoch=train_generator.samples//batch_size, epochs=epochs, validation_data=validation_generator, validation_steps=validation_generator.samples//batch_size)
classifier.save("emotion_model")

Folder structure

• /train_emotion.py
• /emotions/[test, train]/[angry, disgust, fear, happy, neutral, sad, surprise]/+.jpg

Fitting result

I obtained an accuracy of 0.4873 after 15 epochs, however progression is not over.

Fitting history