Facial Expression Recognition with Keras

Kaggle announced facial expression recognition challenge in 2013. Researchers are expected to create models to detect 7 different emotions from human being faces. However, recent studies are far away from the excellent results even today. That’s why, this topic is still satisfying subject.

Dataset

The both training and evaluation operations would be handled with Fec2013 dataset. Compressed version of the dataset takes 92 MB space whereas uncompressed version takes 295 MB space. There are 28K training and 3K testing images in the dataset. Each image was stored as 48×48 pixel. The pure dataset consists of image pixels (48×48=2304 values), emotion of each image and usage type (as train or test instance).

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Suppose that the dataset is already loaded under the data folder. Herein, we can read the dataset content as mentioned below.

with open("/data/fer2013.csv") as f:
content = f.readlines()

lines = np.array(content)

num_of_instances = lines.size
print("number of instances: ",num_of_instances)

Learning Procedure

Deep learning dominates computer vision studies in recent years. Even academic computer vision conferences are closely transformed into Deep Learning activities. Herein, we would apply convolutional neural networks to tackle this task. And we will construct CNN with Keras using TensorFlow backend.

We’ve already loaded the dataset before. Now, train and test set can be stored into dedicated variables.

x_train, y_train, x_test, y_test = [], [], [], []

for i in range(1,num_of_instances):
try:
emotion, img, usage = lines[i].split(",")

val = img.split(" ")
pixels = np.array(val, 'float32')

emotion = keras.utils.to_categorical(emotion, num_classes)

if 'Training' in usage:
y_train.append(emotion)
x_train.append(pixels)
elif 'PublicTest' in usage:
y_test.append(emotion)
x_test.append(pixels)
except:
print("", end="")

Time to construct CNN structure.

model = Sequential()

#1st convolution layer
model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48,48,1)))
model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))

#2nd convolution layer
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

#3rd convolution layer
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

model.add(Flatten())

#fully connected neural networks
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(num_classes, activation='softmax'))

We can train the network. To complete the training in less time, I prefer to implement learning with randomly selected trainset instances. That is the reason why train and fit generator used. Also, loss function would be cross entropy because the task is multi class classification.

gen = ImageDataGenerator()
train_generator = gen.flow(x_train, y_train, batch_size=batch_size)

model.compile(loss='categorical_crossentropy'
, optimizer=keras.optimizers.Adam()
, metrics=['accuracy']
)

model.fit_generator(train_generator, steps_per_epoch=batch_size, epochs=epochs)

Fit is over. We can evaluate the network.

train_score = model.evaluate(x_train, y_train, verbose=0)
print('Train loss:', train_score[0])
print('Train accuracy:', 100*train_score[1])

test_score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', test_score[0])
print('Test accuracy:', 100*test_score[1])

I’ve got the following results not to fall into overfitting. I faced with overfitting when I increase the epoch.

Test loss: 2.27945706329
Test accuracy: 57.4254667071

Train loss: 0.223031098232
Train accuracy: 92.0512731201

Confusion Matrix

Sure, accuracy should not express right impression for multi class classification problems. Confusion matrix of this model is demonstrated below. Lines represent actual values whereas columns state predictions. I mean that there are 467 angry instances in testset. We can classify 214 angry items correctly. On the other hand, we classified 9 items as disgust but these items are actual angry ones.

	Angry	Disgust	Fear	Happy	Sad	Surprise	Neutral
Angry	214	9	53	30	67	8	86
Disgust	10	24	9	2	6	0	5
Fear	45	2	208	29	89	45	78
Happy	24	0	40	696	37	18	80
Sad	65	3	83	56	285	10	151
Surprise	7	1	42	27	9	303	26
Neutral	45	2	68	65	88	8	331

Basically, scikit-learn produces that confusion matrix.

from sklearn.metrics import classification_report, confusion_matrix

pred_list = []; actual_list = []

for i in predictions:

pred_list.append(np.argmax(i))

for i in y_test:

actual_list.append(np.argmax(i))

confusion_matrix(actual_list, pred_list)

Face detection

Images are already cropped and just facial area are focused on in the train set. This is not a must but we should detect faces of the custom testing images and feed just facial areas to the neural networks model. This will increase the accuracy dramatically.

There are several face detection solutions. OpenCV offers haar cascade and single shot multibox detector (SSD). Dlib offers Histogram of Oriented Gradients (HOG) and Max-Margin Object Detection (MMOD). Finally Multi-task Cascaded Convolutional Networks (MTCNN) is a common solution for face detection. Herein, haar cascade and HoG are legacy methods whereas SSD, MMOD and MTCNN are deep learning based modern solutions. You can see the detection performance of those models in the following video.

Here, you can watch how to use different face detectors in Python.

Here, retinaface is the cutting-edge face detection technology. It can even detect faces in the crowd and it finds facial landmarks including eye coordinates. That’s why, its alignment score is very high.

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Testing

Let’s try to recognize facial expressions of custom images. Because only error rates don’t express anything.

img = image.load_img("/data/pablo.png", grayscale=True, target_size=(48, 48))

x = image.img_to_array(img)
x = np.expand_dims(x, axis = 0)

x /= 255

custom = model.predict(x)
emotion_analysis(custom[0])

x = np.array(x, 'float32')
x = x.reshape([48, 48]);

plt.gray()
plt.imshow(x)
plt.show()

Emotions stored as numerical as labeled from 0 to 6. Keras would produce an output array including these 7 different emotion scores. We can visualize each prediction as bar chart.

def emotion_analysis(emotions):
objects = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')
y_pos = np.arange(len(objects))

plt.bar(y_pos, emotions, align='center', alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('percentage')
plt.title('emotion')

plt.show()

If you watch the famous Netflix series Narcos, then you would be familiar with the following picture. The following picture of Pablo Escobar is taken in a police station when he was taken into custody. It seems that the model we’ve constructed can successfully recognize Pablo in happy mood.

Secondly, we will test the scene of Marlon Brando acting in Godfather as Don Corleone. Corleone cries at dead body of his son’s elbow. It seems that the model can recognize Brando’s facial expression, too.

What’s more, Hugh Jackman comes to my mind as always angry figure. That’s why, I would like to test him. Especially, I choose a picture of Jackman from X-Men as Wolverine. Result seems very successful.

Finally, art authorities still cannot come to mutual agreement for Mona Lisa’s emotion. Network says that Mona Lisa is in neutral mood.

Real time solution

Besides, we can apply emotion analysis on a video streaming or web cam capturing. I’ve written a dedicated blog post about this subject. Its demo is shown below.

Web cam

Me and my colleagues try to act all emotion classes. As seen, this implementation runs very fast.

Video streaming

Remember the testimony of Mark Zuckerberg after Cambridge Analytica scandal. Facebook lost 134 billion dollar after this news. This makes unhappy anyone just like Mark as detected below.

Conclusion

So, we’ve constructed a CNN model to recognize facial expressions of human beings. Model produces 57% accuracy on test set. That can be acceptable because winner of kaggle challenge has got 34% accuracy.

Processing detected faces instead of the entire image would increase accuracy. That’s a little trick. I crop the faces manually before running network.

The entire code of the project is pushed on GitHub. Also, you might want to apply transfer learning and use pre-trained weights. Pre-trained weights and pre-constructed network structure are pushed on GitHub, too.

This post covers my custom design for facial expression recognition task. I can improve the accuracy from 57% to 66% with Auto-Keras for the same task.

If you interested in this post, you might be interested in deep face recognition.

Python library

Herein, deepface is a lightweight facial analysis framework covering both face recognition and demography such as age, gender, race and emotion. If you are not interested in building neural networks models from scratch, then you might adopt deepface. It is fully open-source and available on PyPI. You can make predictions with a few lines of code. It also supports real-time implementations as well.

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Here, you can watch a how to apply facial attribute analysis in python with a just few lines of code.

You can run deepface in real time with your web cam as well.

Also, deepface has its own ui built with react js for real time facial attribute analysis purposes.

Anti-Spoofing and Liveness Detection

What if DeepFace is given fake or spoofed images? This becomes a serious issue if it is used in a security system. To address this, DeepFace includes an anti-spoofing feature for face verification or liveness detection.

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