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.

Using deep autoencoders for facial expression recognition

“…Authors in [8], [11] have used Support Vector Machines (SVMs) for FER. In [12], [13], authors utilized Hidden Markov Model (HMM) for FER. HMMs are mostly used for frame-level features to handle sequential data.…”

Recognition of facial expressions based on salient geometric features and support vector machines

“…Among the appearance-based features, local binary pattern (LBP) is widely used recognizing facial expressions [13][14][15][16][17]. Similarly, local Gabor binary pattern [16], histogram of orientation gradient [18], Gabor wavelets representation [17], scale invariant feature transform (SIFT) [19], non-negative matrix factorization (NMF) based texture features [20,21], linear discriminant analysis (LDA) [22], independent component analysis (ICA) [22] etc., are also widely used appearance-based feature for the recognition of facial expressions.…”

Face Behavior Recognition Through Support Vector Machines

“…www.ijacsa.thesai.org Identifying facial features from many combinations that can be derived from these points is another important process which needs more investigation. Therefore, the most commonly used features are selected [3,10,11]. Figure 5 shows the 15 facial features that are derived from 27 used facial feature points.…”