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Multimodal Bio-metric recognition with Fusion on Face Finger Iris Ear and Vein Pattern Based Recognition

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Security access control systems and forensic applications. Performance of conventional unimodal biometric systems is generally suffered due to the noisy data, non universality and intolerable error rate. In propose system, multi layer Convolutional Neural Network (CNN) is applied to multimodal biometric human authentication using face, palm vein and fingerprints to increase the robustness of system. For the classification linear Support Vector Machine classifier is used. For the evaluation of system self developed face, palm vein and fingerprint database having 4,500 images are used. The performance of the system is evaluated on the basis of % recognition accuracy, and it shows significant improvement over the unimodal-biometric system and existing multimodal systems.



Nowadays, biometric recognition is generally used in many modern human authentication systems such as criminal identification systems, forensic applications and secure access control systems etc. Biometric authentication is method of automatically recognizing the human being by using some computational algorithm with the aid of biometric features stored in database. The features are extracted for various internal or external biological parts of the human body such as face, fingerprint, eye retina, hand vein, finger vein, lips, palm prints, lips, iris, voice, dental radiographs and hand radiographs etc . Biometric authentications are categorized in unimodal and multimodal biometric authentication systems. Unimodal biometric authentication system uses single biological entity which is less secure, less reliable and has inadequate usability. Whereas, multimodal biometric authentication systems use multiple biological entities for the authentication which are more reliable, secure, accurate and robust. In multimodal biometric system the fusion of data can be done at sensor level, feature level, classifier level and rank level. Generally, biometrics systems are classified in static and dynamic biometric systems. Static features of the humans are rigid over the time such as fingerprint, iris, face etc. While, dynamic features of the humans are changing over the time such as voice, electrocardiogram, keystroke and touch dynamics etc. Efficiency, accuracy, robustness, security and privacy are themajor performance evaluation parameters for biometrics recognition systems


Existing systems:

  • Edge detection
  • Segmentation
  • Feature vector


Draw backs of Existing systems:

  • Existing is done using Finger printing .Finger printing is that much notflexible because we can make duplicates of fingers and bluff people. It isnot that much efficient.
  • Only the spatial domain is calculated.
  • We will be using PCA i.e. Principal Component Analysis algorithm tofind out co-variance and variance.


Proposed System:

In proposed methodology, three layers of CNN are applied to the face, finger print and palm vein images. To minimize the computation time original images are converted to the gray scale image from RBG color space. Fully connected layer is applied after third CNN layer. The neurons obtained at the fully connected layers of CNN applied for the face, palm vein and finger prints are concatenated for feature level fusion before the classification. The features are given to the one verses all multiclass linear SVM classifier. The performance of the system is evaluated on the basis of the % recognition rate. The flow diagram of the proposed methodology . The convolutional neural network consists of basic four layers such as convolution layer, rectified linear unit layer (ReLU), max pooling layer and fully connected layer for futuristic security applications


Block Diagram:

Multimodal Bio-metric recognition with fusion on Face Finger Iris Ear And Vein Pattern Based Recognition





  • Sequential Haar coefficient requires only two bytes to store each of theextracted coefficients.
  • The cancellation of the division in subtraction results avoids the usage of

decimal numbers while preserving the difference between two adjacentpixels.

  • This system gives more security compared to uni-modal system because

of two biometric features



  • Pattern Recognition
  • Authentication


Tools Required:

  • MATLAB 7.5 and above versions
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