INTEGRATING VOICE AND FINGERPRINT BIOMETRICS FOR IDENTIFYING GENDER AND PREDICTING AGE
Mohammad Musthafa
, G. Pramod Kumar , P.Hari Babu , Mrs. B. Keerthi Chaitanya
Biometrics, Voice Processing, Fingerprint Analysis, CNN, RNN, LSTM, Age Prediction, Gender Classification, Multimodal Fusion, MFCC.
—Biometric systems provide reliable solutions for
identity verification and demographic analysis. Single-modality
systems, however, face challenges in achieving high accuracy
under diverse real-world conditions. This paper proposes an
integrated multimodal approach combining voice and
fingerprint biometrics for age and gender prediction.
Leveraging Convolutional Neural Networks (CNNs) for
fingerprint-based gender classification and Recurrent Neural
Networks (RNNs) with Long Short-Term Memory (LSTM)
units for voice-based age estimation, the system achieves 90%
gender prediction accuracy and 85% age group classification
accuracy. Score-level fusion of both modalities improves
accuracy by approximately 10% over single-modality
baselines, validating the effectiveness of multimodal biometric
integration.
"INTEGRATING VOICE AND FINGERPRINT BIOMETRICS FOR IDENTIFYING GENDER AND PREDICTING AGE", JETNR - JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (www.JETNR.org), ISSN:2984-9276, Vol.4, Issue 4, page no.c569-c574, April-2026, Available :https://rjpn.org/JETNR/papers/JETNR2604341.pdf
Volume 4
Issue 4,
April-2026
Pages : c569-c574
Paper Reg. ID: JETNR_233997
Published Paper Id: JETNR2604341
Downloads: 00016
Research Area: Science and Technology
Country: Hyderabad , Telangana , India
ISSN: 2984-9276 | IMPACT FACTOR: 9.87 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.87 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: RJPN (IJPublication) Janvi Wave
