An Empirical Evaluation of the Local Texture Description Framework-Based Modified Local Directional Number Pattern with Various Classifiers for Face Recognition

Abstract

Texture is one of the chief characteristics of an image. In recent years, local texture descriptors have garnered attention among researchers in describing effective texture patterns to demarcate facial images. A feature descriptor titled Local Texture Description Framework-based Modified Local Directional Number pattern (LTDF_MLDN),, and hand gesture detection [10]. However, in situations where the exact points are known, basic SVM models can be used [11]. Though the SVM dominates existing computational intelligence techniques in the field, it faces certain challenges in terms of low learning speed, less computational scalability and trivial human intervention. The ELM, a piece of emerging technology, has overcomes several of these issues to perform better than SVM in solving applications in the fields of regression and classification [12]. A key characteristic of this classifier is that the hidden layer needs no tuning [13]. In the NNC classification algorithm, the kernel part is the matching process which defines the resemblance or disparity between the features of a probe image and that of a gallery image. There exist numerous distance measures for the matching process. An empirical evaluation for texture classification was carried out earlier by Rubiner et al. [14]. The success of the NNC depends heavily on the distance measures used, given that the performance of these different distance metrics depends on factors such as the datasets, experimental setup and applications. Hence, in this paper, the NNC's performance is reviewed with different distance metrics such as chi-square, Manhattan, G-statistics, Euclidean and Minkowski. Motivation and JustificationFeatures are essential to characterize face images. Facial features include facial skin color, skin texture, the geometrical shape of facial components and so on. Of the existing facial features, skin texture is found to work well in face recognition with different facial variations. Texture description can be done in two ways. One describes the entire face, a gray level co-occurrence matrix [15] being an instance of such a method. The other describes a local region in the face and collects all the local information available into a histogram to represent the whole face. The second type of method, known as a local texture descriptor, is effective with pose variation and scaling, the local binary pattern (LBP) [16] being an instance of such. This descriptor is computationally simple and encodes rotational invariant local texture patterns. The LBP encodes the first-order circular derivative of local patterns by concatenating binary gradient directions [17]. Several local texture descriptors have been applied for face recognition. Depending on whether the descriptors encode intensity transition or the directional information of the local neighborhood's intensity transition, they can be categorized into three. The first category, such as LBP, encodes the local neighborhood's intensity transition while the second, such as the local directional number...

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