Ajay Kushwaha¹, Tushar Kumar Pandey², B. Laxmi Kantha³, Prashant Kumar Shukla⁴, Sheo Kumar⁵, Rajesh Tiwari^5,*

¹ Professor, Department of Computer Science and Engineering, Rungta College of Engineering and Technology, R1, Bhilai, CG, 490024, India

² Assistant Professor (Computer Science), College of Community Science, Central Agricultural University, Tura, Meghalaya, India

³Associate Professor, Dept. of IT, St. Martin's Engineering College, Secunderabad, Telangana, India

⁴Associate Professor (Research), Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-522302, Andhra Pradesh, India

⁵Professor (CSE), CMR Engineering College, Hyderabad, Telangana, India

Emails: ajay.kushwaha@rungta.ac.in; tusharkumarpandey@gmail.com; drblaxmiit@smec.ac.in; prashantshukla2005@kluniversity.in; sheo2008@gmail.com; drrajeshtiwari20@gmail.com

Abstract

Different biological data may be used to identify people in this investigation. The system uses complex multimodal fusion, feature extraction, classification, template matching, adjustable thresholding, and more. A trustworthy multimodal feature vector (B) is created using the Multimodal Fusion Algorithm from voice, face, and fingerprint data. The key objectives are weighing, normalizing, and extracting characteristics. Complex feature extraction algorithms improve this vector and ensure its accuracy and reliability. Hamming distance is utilized in template matching for accuracy. Support vector machines to ensure classification accuracy. The adaptive threshold technique adjusts option limits based on the biology score mean and standard deviation when external conditions change. A thorough look at the research shows how algorithms operate together and how vital each aspect is for locating criminals. Change the multimodal fusion weights for optimum results. Thorough research using tables and photographs revealed that the fingerprint approach is optimal. Fast, simple, and precise technologies may enable new unlawful recognition tools. The adaptive thresholding algorithm's multiple adaptation steps allow the system to adjust to diverse study circumstances. The Multimodal Biometric Identification System is a cutting-edge leader in its area and provides a trustworthy, practical, and customizable research choice. This novel strategy is at the forefront of criminal recognition technology and has been supported by ablation research. It affects reliability, accuracy, and adaptability.

Keywords: Multimodal Biometrics; Criminal Identification; Multimodal Fusion Algorithm; Feature Extraction; Classification Algorithm; Template Matching; Adaptive Thresholding; Ablation Study; Forensic Technology