Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

Rohit Raja,Teekam Singh,Kamred Udham Singh,Dinesh Gupta,Chetan Swarup,Neeraj Varshney,Abhishek Kumar,Swati Jain

doi:10.32604/cmc.2022.022904

Abstract

As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering. The close vicinity of minimum distance measurements is done for these clusters, mapped for proper classification and decision process of behavioral pattern. To identify the data acquired, support vector machines and artificial neural networks are utilized. A method known as an adaptive-unidirectional associative memory (AUTAM) was used to map one side of a human face to the other side of the same subject. The behavioral pattern has been detected based on two-class problem classification, and the decision process has been done using a genetic algorithm with best-fit measurements. The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET, Multi-PIE, Yale Face database, RTR, CASIA, etc. The complexity measures have also been calculated under worst-case and best-case situations.

Highlights

To detect the behavioral pattern of any subject is the most challenging task, especially in the defense field
Bouzas et al [1] used a similar method in his method for dimensional space reducing switching amount based on the requirement of mutual information between the altered data and their associated class labels
The categorization in this study is based on four geometrical characteristics taken from human-face data for left- and right-side-vision s

Summary

Introduction

To detect the behavioral pattern of any subject (human) is the most challenging task, especially in the defense field. The current study examines similar challenges using a side-by-side perspective of human-face data. Only a few researchers have used side visions of human faces to identify behavioral traits. Most research has been conducted using frontal-vision data of human faces, either for face recognition or as a biometric characteristic assessment. Very few types of research have been carried out to detect behavioral patterns. Several significant improvements have been made before identifying human faces from the side (parallel to the picture plane), using a five-degree switching mechanism in regression or decreasing step method. Later on, [2] enhanced the work performance by using descriptions to describe human-face pictures and a clustering algorithm to choose and classify variables for human-face recognition

Methods

Results

Conclusion