Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

Sensors

Muhammad Hameed Siddiqi¹  Sungyoung Lee¹  Young-Koo Lee¹  Adil Mehmood Khan² 

[1] UC Lab, Department of Computer Engineering, Kyung Hee University, Yongin-Si 446-701, Korea; E-Mails:;Division of Information and Computer Engineering, Ajou University, Suwon 443-749, Korea; E-Mail:

关键词: face detection;    GHE;    facial expressions;    PCA;    ICA;    LDA;    HMMs;   

DOI  :  10.3390/s131216682

来源: mdpi

【 摘 要 】

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.