Facial Expression Recognition of Home Service Robots

Abstract

It is of great significance that a home service robot can recognize facial expressions of a human being. This thesis suggests that features of facial expressions be extracted with PCA, and facial expressions be recognized by distance-based Hashing K-nearest neighbor classification. First, Haar-like feature and AdaBoost algorithm is adopted to detect a face and preprocess the face image; then PCA is applied to extract features of the facial expression, those features will be inserted into the hash table; finally, the facial expression can be recognized by K-nearest neighbor classification algorithm. As concluded, recognition efficiency can be greatly improved after reconstructing the feature database into hash tables.

Discriminative bit selection hashing in RGB-D based object recognition for robot vision

“…However, with the development of feature extraction methods for RGB-D data, especially the development of deep learning-based feature extraction methods, storage cost for these features increases dramatically (Gupta et al, 2014;Zeng et al, 2018, Schwarz et al, 2015Wu et al, 2016 ). Hence, to meet the requirements of high-speed computation for robotic applications, some researchers use hashing learning methods to the research field of robot vision (Yu et al, 2016;Chen et al, 2013). Hashing learning aims at representing original data with binary codes, which can improve the efficiency of data analysis (Weiss et al, 2008;Gong et al, 2013;Liu et al, 2018).…”