Facial expressions classification and false label reduction using LDA and threefold SVM

Abstract Representation and classification of multi-dimensional data are current key research areas. The representation of data in two classes is more feasible than multi-class representations because of the inherent quadratic complexity in existing techniques. Erroneous assignment of class labels affects separation boundary and training time complexity. In this paper, multi-dimensional data is handled using linear discriminant analysis (LDA) and threefold support vector machine (SVM) techniques to reduce the complexity and minimize false labeling. A facial expression application is proposed in which six natural expressions are used as multi-class data. Face image is divided into seven triangles on the basis of two focal points. A combined local and global feature descriptor is generated. Discrete Fourier transform is applied and processed with LDA to obtain discriminant features and accurately map an input feature space to an output space. To evaluate the system performance, Japanese Female Facial Expression, FER-2013 and Cohn–Kanade DFAT datasets are used. The obtained results show that multi-class data hyper plane using LDA and threefold SVM approach is effective and simple for quadratic data analysis

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