Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

• Published in: Sensors (Basel, Switzerland) Vol. 13; no. 12; pp. 16682 - 16713
• Main Authors: Siddiqi, Muhammad, Lee, Sungyoung, Lee, Young-Koo, Khan, Adil, Truc, Phan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.12.2013; Molecular Diversity Preservation International (MDPI)
• Subjects: Access control; Accuracy; Computer engineering; Datasets; Discriminant Analysis; Face - physiology; face detection; Facial Expression; facial expressions; GHE; HMMs; Humans; ICA; Image retrieval; LDA; Pattern Recognition, Physiological - physiology; PCA; Performance evaluation; Sensors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s131216682

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.