Image Processing Reference
In-Depth Information
of the FER works used principal component analysis (PCA), which is really well known for di-
mension reduction and used in many earlier works. In Padget and Cotrell [ 3 ], PCA was used
to recognize facial action units (FAUs) from the facial expression images. In Donato et al. [ 5 ]
as well as Ekman and Priesen [ 6 ] , PCA was used for FER with the facial action coding system.
Very recently, independent component analysis (ICA) has been extensively utilized for FER
based on local face image features [ 5 , 10 - 21 ] . In Bartlet et al. [ 14 ] , the authors used ICA to
extract local features and then classified several facial expressions. In Chao-Fa and Shin [ 15 ] ,
ICA was used to recognize the FAUs. Besides ICA, local binary paterns (LBP) has been used
lately for FER [ 22 - 24 ] . The main property of LBP features is their tolerance against illumin-
ation changes as well as their computational simplicity. Later on, LBP was improved by fo-
cusing on face pixel's gradient information and named as local directional pattern (LDP) to
represent local face features [ 25 ] . As like as LBP, LDP features also have the tolerance against
illumination changes but they represent much robust features than LBP due to considering the
gradient information for each pixel as aforementioned [ 25 ] .
Thus, LDP can be considered to be a robust approach and hence can be adopted for FER.
To make LDP facial expression features more robust, linear discriminant analysis (LDA) can
be applied as LDA is a strong method to be used to obtain good discrimination among the
face images from different expressions by considering linear feature spaces. Hidden Markov
model (HMM) is considered to be a robust tool to model and decode time-sequential events
[ 21 , 26 - 28 ] . Hence, HMM seems an appropriate choice to train and recognize features of difer-
ent facial expressions for FER.
For capturing face images, RGB cameras are used most widely but the faces captured
through a RGB camera cannot provide the depth of the pixels based on the far and near parts
of human face in the facial expression video where the depth information can be considered to
contribute more to extract efficient features to describe the expression more strongly. Hence,
depth videos should allow one to come up with more efficient person independent FER.
In this chapter, a novel FER approach is proposed using LDP, PCA, LDA, and HMM. Local
LDP features are first extracted from the facial expression images and further extended by
PCA and LDA. These robust features are then converted into discrete symbols using vector
quantization and then the symbols are used to model discrete HMMs of different expressions.
To compare the performance of the proposed approach, different comparison studies have
been conducted such as PCA, PCA-LDA, ICA, and ICA-LDA as feature extractor in combina-
tion with HMM. The experimental results show that the proposed method shows superiority
over the conventional approaches.
2 Depth Image Preprocessing
The images of different expressions are captured by a depth camera [ 29 ] where the camera
generates RGB and distance information (i.e., depth) simultaneously for the objects captured
by the camera. The depth video represents the range of every pixel in the scene as a gray level
intensity (i.e., the longer ranged pixels have darker and shorter ones brighter values or vice
versa). Figure 1 shows the basic steps of proposed FER system.
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