Multimodal Biometric Authentication System
This project is based on Score level fusion of two biometric traits: Face and Finger
1) For face recognition we have used Principle Component Analysis (i.e. Eigen vector generation) and for fingerprint recognition also we have used the concept of eigen vectors.
2) For Face recognition we have used Open CV library (for some basic image processing operations)
3) For finger print we have used Open Source AFIS library
4) Both Face and Finger modules generate scores which are fused using Sum-Rule based weighted equation.
5) This project is completely finished (100% WORKING)
6) Language used to develop: C# (.net framework)
7) For face we have used C++
8) You can download this project from the following links:
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9) This project can be used for Research work and can be used for reference. This project work is my own work with some open source content.
10) Price for the source code is negotiable.
Detailed Description:
Biometric systems make use of the physiological and/or behavioral traits of individuals, for recognition purposes. These traits include ï¬ngerprints, 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. These sources may be multiple sensors for the same biometric (e.g., optical and solid-state ï¬ngerprint sensors), multiple instances of the same biometric (e.g., ï¬ngerprints from different ï¬ngers of a person), multiple snapshots of the same biometric (e.g., four impressions of a user’s right index ï¬nger), multiple representations and matching algorithms for the same biometric (e.g., multiple face matchers like PCA and LDA), or multiple biometric traits (e.g., face and ï¬ngerprint).
A Unimodal Biometric System (UBS) is usually more cost-efficient than a multimodal biometric system. However, it may not always be applicable in a given domain because of the limitations and problems like skin dryness, disease, data quality, pressure, dirt, oil, etc. Implementing an authentication based on weighted multimodal system gives not only high efficiency and performance but also allows the administrator to adjust ratio of weights as required. Generally feature matching or projecting input on template generates a score which may be non homogeneous. So in that case to fuse two or more traits, score level normalization (numerical scaling) is performed to overcome the limitation of incompatibility of scores. Whereas in our system; the input is continuously projected on the template to record % (percentage) of accuracy or confidence based on the least distance (Euclidean Distance) measurement in finding neighbors (specifically in case of face verification). We record a hundred values; and, in a divide and conquer fashion, mean accuracy scores are stored. These scores are then multiplied with a floating point number ‘n’ typically less than 1, which are then added with the multiplication of another biometric score and ‘1-n’.
For face verification we have used High quality 1/4 CMOS sensor- 480K pixels (Interpolated 8M pixels still image) and for reading finger prints we used optical sensor with 0.14 sec (continuous) / 0.20 sec (snap-shot) imaging speed. Face verification is based on the fundamental concept of 2D model i.e. Principal Component Analysis. It is a mathematical procedure that performs a dimensionality reduction by extracting the principal components of the multi-dimensional data. Fingerprint verification is based on minutiae extraction and Eigen vectors formation.
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