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Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems
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 extra...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892857/ https://www.ncbi.nlm.nih.gov/pubmed/24316568 http://dx.doi.org/10.3390/s131216682 |
Sumario: | 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. |
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