Used Multimodal Biometric Recognition for Windows?


Multimodal Biometric Recognition Analysis

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Not written by CNET Staff.

Multimodal Biometric Recognition is a free productivity software for Windows that facilitates advanced biometric authentication methods. This program supports various modalities, including fingerprint, facial, and voice recognition, allowing users to implement a robust security system for their applications. Its user-friendly interface simplifies the integration of biometric features into existing workflows, making it suitable for both individual and organizational use.

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The software offers flexibility in choosing biometric modalities, ensuring that users can select the most effective method for their specific needs. Additionally, it provides tools for data management and analysis, enhancing the overall efficiency of biometric recognition processes. With its focus on security and ease of use, Multimodal Biometric Recognition stands out as a valuable asset for enhancing productivity through secure access control.


Used Multimodal Biometric Recognition for Windows?


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Full Specifications

GENERAL
Release
Latest update
Version
1.0
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows NT
  • Windows Server
  • Windows 98
  • Windows 7
  • Windows 8
  • Windows XP
  • Windows ME
  • Windows 10
  • Windows 2003
  • Windows Vista
  • Windows 2000
Additional Requirements
Matlab
POPULARITY
Total Downloads
719
Downloads Last Week
0

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Program available in other languages


Last Updated


Developer’s Description

Get Matlab source code to recognize individuals' biometric traits.
Biometric systems make use of the physiological or behavioral traits of individuals, for recognition purposes. These traits include fingerprints, hand-geometry, face, voice, iris, retina, gait, signature, palm-print, ear, etc. Biometric systems that use a single trait for recognition (i.e., unimodal biometric systems) are often affected by several practical problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multimodal biometric systems overcome some of these problems by consolidating the evidence obtained from different sources. Researchers have shown that the use of multimodal biometrics provides better authentication performance over unimodal biometrics. Biometric fusion can be performed at image level, feature level, match score level, decision level, and rank level.

We have developed a multimodal biometric system that efficiently combines fingerprint, iris and palmprint recognition. Extracted features are combined and a final score is computed for classification. Code has been tested with CASIA Iris Image Database Version 1.0 and CASIA Palmprint Image Database. Fingerprint database used in our experiments was a collection of fingerprint images taken with an UPEK swipe fingerprint reader with capacitive sensor and USB 2.0 connection. Database is 16 fingers wide and 8 impressions per finger deep (totally 128 fingerprints). Other biometric modalities are available on request.


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AI Assisted Content Disclosure

Content created and reviewed by Softonic with information obtained from Luigi Rosa, using AI.

CNET's editorial team was not involved in the creation of this content. Opinions, analysis and reviews were not provided by CNET.