Cascade‐based Multimodal Biometric Recognition System with Fingerprint and Face

Abstract

In recent years, biometrics has played a vital role in protecting users' privacy and enabling secure authentication. Multimodal security, comprised of identity cards with attached unique passwords, is used to authenticate the genuine or imposter person; however, it is not a perfect security framework. Biometric traits are unique to every individual and hence proved to be very secure. This paper proposes a hybrid approach by combining cascaded and fusion‐based multimodal biometric framework using fingerprint and face traits. The fingerprint and face features are extracted using the minutiae feature extraction algorithm and principal component analysis (PCA) algorithm. The hybrid approach is applied to a self ‐built database of around 450 fingerprints and 450 face images. The performance of the proposed hybrid system is determined using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), and Accuracy evaluation parameters. The unimodal system of fingerprint and face at Level I and Level II yields an accuracy of 100% at a threshold value of 0.42 and 0.54, respectively. The multimodal system at Level III delivers an accuracy rate of 93.70%, 99.20%, 97.85%, and 99.50% at a threshold value of 0.75, 0.60, 0.30, and 0.60 in sum level fusion, product level fusion, min level fusion, and max level fusion schemes, respectively.

Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey)

“…Furthermore, the database that has been used is SDUMLA-HMT datasets [43] which contain an actual multimodal biometric database of the face and fingerprint pictures. In [44], the authors are used a hybrid method integrating cascaded and fusion-based multimodal biometric frameworks employing fingerprint and facial characteristics. Furthermore, this hybrid method is used on a self-created dataset of approximately 450 facial pictures and 450 fingerprints.…”

“…Table 1 summarizes all the methods that have been used in multimodal biometric systems for face and fingerprints from 2017 to 2021 with their databases. public database [42] RF, CNN, and Softmax SDUMLA-HMT datasets [44] hybrid integrating cascaded and fusion-based multi-biometric Hybrid is used on a self-created dataset…”

A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection

“…Tomar et al proposed a hybrid approach that combines fingerprints and facial images in a multimodal biometric framework. Using cascaded and fusion-based techniques, the system achieves high accuracy rates at different fusion levels (302).…”

A Multimodal Biometric Authentication System Using the Exponential Water Wave Optimization Algorithm

“…In comparison to other classifiers, accuracy is higher here. Peeyush Tomar and Ram C. Singh [33] created a hybrid technique for integrating fingerprint and facial features, incorporating a cascaded and fusion-based multimodal biometric architecture. The minutiae feature extraction technique and Principal Component Analysis ( PCA) are used to extract fingerprint and facial features.…”