Facial-Tattoo Fusion for Identification: A Machine Learning Paradigm

Paper ID: EIJTEM_2026_13_1_93-102

Author's Name: Vijay Kiran Katikala

Volume: 13

Issue: 1

Year: 2026

Page No: 93-102

Abstract:

This study suggests a new method for identity proof by mixing face recognition and tattoo patterns, leveraging machine learning techniques. Traditional biometric systems focus on facial features for identification, but tattoos, which are unique and personal, can offer an additional layer of security. In this study, we explore the fusion of facial and tattoo data using convolutional neural networks (CNNs) and advanced image processing methods. Our model is designed to extract and integrate features from both modalities, enhancing identification accuracy in diverse environments. We demonstrate the effectiveness of the proposed paradigm through experiments on a custom dataset, showing that fusion-based identification outperforms conventional facial recognition systems. The results underline the potential of this hybrid approach in enhancing security and reliability for applications ranging from law enforcement to personalized services.

Keywords: Facial Recognition, Tattoo Identification, Machine Learning, Biometric Fusion, Identity Verification.

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