IMPROVING CNN FEATURES FOR FACIAL EXPRESSION RECOGNITION

Abstract

<span class="fontstyle0">Abstract </span><span class="fontstyle2">Facial expression recognition is one of the challenging tasks in computer<br />vision. In this paper, we analyzed and improved the performances both<br />handcrafted features and deep features extracted by Convolutional Neural<br />Network (CNN). Eigenfaces, HOG, Dense-SIFT were used as handcrafted features.<br />Additionally, we developed features based on the distances between facial<br />landmarks and SIFT descriptors around the centroids of the facial landmarks,<br />leading to a better performance than Dense-SIFT. We achieved 68.34 % accuracy<br />with a CNN model trained from scratch. By combining CNN features with<br />handcrafted features, we achieved 69.54 % test accuracy.<br /></span><span class="fontstyle0">Key Word</span><span class="fontstyle3">: </span><span class="fontstyle2">Neural network, facial expression recognition, handcrafted features</span> <br /><br />