Abstract
Facial expressions are predominantly important in the social interaction as they convey the personal emotions of an individual. The main task in Facial Expression Recognition (FER) systems is to develop feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting distinctive features, Radial Cross Pattern (RCP), Chess Symmetric Pattern (CSP) and Radial Cross Symmetric Pattern (RCSP) feature descriptors have been proposed and are implemented in a 5 times 5 overlapping neighborhood to overcome some of the limitations of the existing methods such as Chess Pattern (CP), Local Gradient Coding (LGC) and its variants. In a 5 times 5 neighborhood, the 24 pixels surrounding the center pixel are arranged into two groups, namely Radial Cross Pattern (RCP), which extracts two feature values by comparing 16 pixels with the center pixel and Chess Symmetric Pattern (CSP) extracts one feature value from the remaining 8 pixels. The experiments are conducted using RCP and CSP independently and also with their fusion RCSP using different weights, on a variety of facial expression datasets to demonstrate the efficiency of the proposed methods. The results obtained from the experimental analysis demonstrate the efficiency of the proposed methods.
Highlights
Facial expressions provide valuable information about a person by reflecting his psychological characteristics, which provide an important means for effective communication [66]
Upon considering the diagonal pixels in four directions, the experimental results showed an enhanced recognition accuracy. – The proposed methods have been evaluated with different weights to find out the optimal recognition accuracy. – To evaluate and validate the efficiency of the proposed methods, the experiments are conducted on a variety of facial expression datasets which include datasets captured in the lab environment, dataset in the wild and on an animated facial expression dataset. – The proposed methods, which are non-parametric methods, outperformed the standard existing methods proving the robustness of the proposed descriptors
The main objective of Facial Expression Recognition (FER) systems is to develop feature descriptors that could accurately classify the facial expressions into various categories
Summary
Facial expressions provide valuable information about a person by reflecting his psychological characteristics, which provide an important means for effective communication [66]. A proper feature extraction technique which could effectively capture expression-specific changes is essential for an FER system [66]. For accurate classification in FER, it is essential to capture those minute information related to specific expressions. Based on the literature studies, the texture-based feature descriptors have been proven to be effective for extracting valuable features from a facial image by manipulating the neighborhood pixel relationship for detecting and capturing minute details in an image [35,56,57]. The remainder of the paper is structured as follows: the related work in the field of FER is summarized, and a brief review of existing descriptors that are a basis for the proposed methods are mentioned in the third section. The concluding remarks and the suggestions for further analysis and study are mentioned in the last section
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