![]() ![]() has been found a better range for neural network models in computer vision problems.įace = įace = np.asarray(face).reshape(width, height)įace = cv2.resize(face.astype('uint8'),image_size)Įmotions = pd.get_dummies(data).as_matrix() Further, subtraction by 0.5 and multiplication by 2 changes the range to. Images is scaled to by dividing it by 255. def preprocess_input: It is a standard way to pre-process images by scaling them between -1 to 1. def load_fer2013 : It reads the csv file and convert pixel sequence of each row in image of dimension 48*48. There are two definitions in the code snippet here:ġ. The below code loads the data-set and pre-process the images for feeding it to CNN model. The contents of this string a space-separated pixel values in row major order Loading FER Data-set The “pixels” column contains a string surrounded in quotes for each image. The “emotion” column contains a numeric code ranging from 0 to 6, inclusive, for the emotion that is present in the image. train.csv contains two columns, “emotion” and “pixels”. ![]() The training set consists of 35,888 examples. ![]() The task is to categorize each face based on the emotion shown in the facial expression in to one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). The faces have been automatically registered so that the face is more or less centered and occupies about the same amount of space in each image. The data consists of 48×48 pixel gray scale images of faces. One can download the facial expression recognition (FER) data-set from Kaggle challenge here.
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