Facial attribute space compression by latent human topic discovery
Journal
2014 ACM Multimedia Conference
Pages
1157-1160
ISBN
9781450330633
Date Issued
2014
Author(s)
Abstract
Facial attribute is important information for a variety of machine vision tasks including recognition, classification, and retrieval. There arises a strong need for detecting various facial attributes such as gender, age and more which consume more computation and storage resources. Therefore, we propose a compression framework to find fewer significant Latent Human Topics (LHT) to approximate more facial attributes. LHT is a combination of attribute correlation by transferring facial attribute space to compressional space with Singular Value Decomposition (SVD). Using the proposed scheme, we can easily detect the facial attributes from a face image via fast reconstructing the compressed labels automatically detected by a few LHT classifiers. Experimental results show that our system can achieve similar performance with substantially fewer dimensions compared to the original number of facial attributes, and it even shows slight improvements because LHT carry informative attribute correlations learned from data.
Subjects
Attribute correlation; Compression; Facial attribute detection
SDGs
Type
conference paper