Abstract
AbstractFacial 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.
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference74 articles.
1. Aifanti N, Papachristou C, Delopoulos A (2010) The mug facial expression database. In: 11th international workshop on image analysis for multimedia interactive services WIAMIS 10, pp 1–4. IEEE
2. Alphonse AS, Shankar K, Rakkini MJ, Ananthakrishnan S, Athisayamani S, Singh AR, Gobi R (2020)A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification. J Ambient Intell Human Comput 20: 1–17
3. Aneja D, Colburn A, Faigin G, Shapiro L, Mones B (2016) Modeling stylized character expressions via deep learning. In: Asian conference on computer vision. Springer, pp 136–153
4. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
5. Chan TH, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) Pcanet: a simple deep learning baseline for image classification. IEEE Trans Image Process 24(12):5017–5032
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