**Emotional Detection of Face Image**
Alex Torres, Hao Yan
*Electrical & Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, USA*
{alexdtorres91, haoyan123}@gmail.com
**Project Definition:** Using deep neural network models, classification of camera images of faces of various emotion. The dataset includes images of 7 different emotion expression of face
train.csv contains two columns, "emotion" and "pixels". The "emotion" column contains a numeric code ranging from 0 to 6, inclusive, for the emotion that is present in the image. The "pixels" column contains a string of pixel intensities for the image. The contents of this string are space-separated pixel values in row major order. test.csv contains only the "pixels" column and your task is to predict the emotion column. The training set consists of 28,709 examples. The public test set used for the leaderboard consists of 3,589 examples. The final test set, which was used to determine the error rate of models.
**Outcome:** Applying convolutional neural network to identify the emotion from the face image
**Dataset:** The image data can be found in https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data. The data consists of 48x48 pixel grayscale images of faces. 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 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).