A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

Abstract

Abstract-An article introduces a modified architecture of the neo-fuzzy neuron, also known as a "multidimensional extended neo-fuzzy neuron" (MENFN), for the face recognition problems. This architecture is marked by enhanced approximating capabilities. A characteristic property of the MENFN is also its computational plainness in comparison with neuro-fuzzy systems and neural networks. These qualities of the proposed system make it effectual for solving the image recognition problems. An introduced MENFN's adaptive learning algorithm allows solving classification problems in a real-time fashion.

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

“…Neuro-fuzzy can approximate certain types of nonlinear functions well in nature. Therefore, neuro-fuzzy models have been applied in designing control systems, such as the temperature control system for greenhouse [33], an antilock braking system of motor vehicle [34], a water-level control of Utube steam generators in nuclear power plants [35], and so on [3,7,8]. This study have exhibited that proposed methods have better properties than the conventional counter methods in function approximations and realworld benchmark problems.…”

“…Conceiving complementary strengths of neural and fuzzy systems, neuro-fuzzes have been applied to handle numerous real-life problems including control, function approximations, classifications, etc. [1][2][3][6][7][8].…”

The Multidimensional Extended Neo-Fuzzy System and its Fast Learning for Emotions Online Recognition

“…The considered architecture of the ENFN was further developed in [23], where a multidimensional extended neofuzzy neuron (MENFN) (10) we obtain the learning algorithm:…”

Detecting Happiness in Human Face using Unsupervised Twin-Support Vector Machines

“…In training process of laughter/happy, the feature vector displacements were formed by captured MMI, Mahnob-Laughter , CK, CK+ and real time datasets [35][36][37][38][39]. The major facial feature movements of happy is in Group 8 (outer lip mouth region) has 12 features points and Group 2 (corner lip region) has 2 features point is horizontally (x-axis) expanded, given by FAPs.…”

“…In the feature and model based approaches [13][14][15][16], [21][22][23][24], [39][40][41][42] the classification are major role of the real time facial expression system. In that we survey [31][32][33] Twin Support Vector Machines is better performance II.…”