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Facial Expression Recognition based on Keras

Project description

FER

Facial expression recognition.

image

PyPI version Build Status

INSTALLATION

Currently FER only supports Python3.4 onwards. It can be installed through pip:

$ pip3 install fer

This implementation requires OpenCV>=3.2 and Tensorflow>=1.7.0 installed in the system, with bindings for Python3.

They can be installed through pip (if pip version >= 9.0.1):

$ pip3 install tensorflow>=1.7 opencv-contrib-python==3.3.0.9

or compiled directly from sources (OpenCV3, Tensorflow).

Note that a tensorflow-gpu version can be used instead if a GPU device is available on the system, which will speedup the results. It can be installed with pip:

$ pip3 install tensorflow-gpu\>=1.7.0

USAGE

The following example illustrates the ease of use of this package:

from fer.fer import FER
import cv2

img = cv2.imread("justin.jpg")
detector = FER()
print(detector.detect_emotions(img))

Sample output:

[{'box': [277, 90, 48, 63], 'emotions': {'angry': 0.02, 'disgust': 0.0, 'fear': 0.05, 'happy': 0.16, 'neutral': 0.09, 'sad': 0.27, 'surprise': 0.41}]

For recognizing facial expressions in video, the Video class splits video into frames. It can use a local Keras model (default) or Peltarion API for the backend:

from fer.classes import Video

video = Video(video_filename)
# Analyze video, displaying the output
raw_data = video.analyze(detector, display=True)
df = video.to_pandas(raw_data)

The detector returns a list of JSON objects. Each JSON object contains two keys: 'box' and 'emotions':

  • The bounding box is formatted as [x, y, width, height] under the key 'box'.
  • The emotions are formatted into a JSON object with the keys 'anger', 'disgust', 'fear', 'happy', 'sad', surprise', and 'neutral'.

Other good examples of usage can be found in the files example.py and video-example.py located in the root of this repository.

MODEL

FER bundles a Keras model, as well as support for Peltarion API.

The model is a convolutional neural network with weights saved to HDF5 file in the data folder relative to the module's path. It can be overriden by injecting it into the FER() constructor during instantiation with the emotion_model parameter.

LICENSE

MIT License.

CREDIT

This code includes methods and package structure copied or derived from Iván de Paz Centeno's implementation of MTCNN and Octavia Arriaga's facial expression recognition repo.

REFERENCE

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