access icon free Angled local directional pattern for texture analysis with an application to facial expression recognition

Local binary pattern (LBP) is currently one of the most common feature extraction methods used for texture analysis. However, LBP suffers from random noise, because it depends on image intensity. Recently, a more stable feature method was introduced, local directional pattern (LDP) uses the gradient space instead of the pixel intensity. Typically, LDP generates a code based on the edge response values using Kirsch masks. Yet, despite the great achievement of LDP, it has two drawbacks. The first is the static choice of the number of most significant bits used for LDP code generation. Second, the original LDP method uses the 8-neighborhood to compute the LDP code, and the value of the centre pixel is ignored. This study presents angled local directional pattern (ALDP), which is an improved version of LDP, for texture analysis. Experimental results on two different texture data sets, using six different classifiers, show that ALDP substantially outperforms both LDP and LBP methods. The ALDP has been evaluated to recognise the facial expressions emotion. Results indicate a very high recognition rate for the proposed method. An added advantage is that ALDP has an adaptive approach for the selection of the number significant bits as opposed to LDP.

Inspec keywords: perceptrons; face recognition; decision trees; feature extraction; emotion recognition; image texture; support vector machines

Other keywords: feature extraction method; Kirsch Masks; local binary pattern; perceptron; facial expression emotion; image intensity; random forest; LDP method; facial expression recognition; LBP; support vector machine; random noise; centre pixel value; K-nearest neighbour algorithm; KTHTIPS2b; Naive-Bayes; Kylberg; Cohn-Kanade database; texture data set; gradient space; edge response value; angled local directional pattern; decision tree; texture analysis; ALDP; pixel intensity

Subjects: Neural computing techniques; Combinatorial mathematics; Image recognition; Combinatorial mathematics; Computer vision and image processing techniques; Knowledge engineering techniques

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