title: Hierarchical recognition scheme for human facial expression recognition systems.
abstract: 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.
authors: UC Lab, Department of Computer Engineering, Kyung Hee University, Yongin-Si 446-701, Korea. pthtruc@oslab.khu.ac.kr.
authors: Muhammad Hameed
authors: MH
authors: Siddiqi
authors: Sungyoung
authors: S
authors: Lee
authors: Young-Koo
authors: YK
authors: Lee
authors: Adil Mehmood
authors: AM
authors: Khan
authors: Phan Tran Ho
authors: PT
authors: Truc
mesh_terms: D016002:Discriminant Analysis; D005145:Face / Q000502:physiology*; D005149:Facial Expression; D006801:Humans; D046709:Pattern Recognition, Physiological / Q000502:physiology*