A biometric system is based on uni-modal system which is considered as o- accurate and robust. It lacks in robustness, computational complexity, computational time, lack of feature selection, reliability etc. So, there is a need of multi- modal system. Multimodal system is based on two or more than two biometric recognition system. For any biometric recognition system, the basic problem is lack of feature points. Noise in detected information the precision assumes a noteworthy part in acknowledgment of biometrics. Non-Universality – If each individual can show the biometric characteristic for acknowledgment, then the quality is said to be general. Non-comprehensiveness prompts Failure to Enrols (FTE) blunder in a biometric framework. Lack of distinction – Feature separated from various people might be comparable. This absence of uniqueness expands the False Accept Rate (FAR) of a biometric framework.
In this research, Thumbprint feature set and Iris feature set are used for multimodal biometric fusion which has been fused using various techniques. Both biometrics has been chosen as they have worked well when used as uni-modal system. For the reduction of features of iris, BFO has been utilized and for thumbprint GA has been used because GA have capability to optimized feature sets on the basis of fitness function according to our requirement. PCA can produce robust performance when a large amount of feature vectors are available. However, sometimes feature extraction task cannot effectively carry out without data reduction when a feature vector dataset is too huge. Therefore, data reduction techniques can be achieved in many ways such as feature selection. Among those techniques, Genetic Algorithms (GAs) and Bacterial Foraging Optimization (BFO) have proven to be an effective computational method because of best optimal solution based on their fitness function, especially in situations where the search space is highly dimensional. In the end FAR (false acceptance rate), FRR (false rejection rate) and Accuracy has been evaluated to identify the performance of the fusion system.
The significance of the proposed work is to highlight the importance of the optimization techniques in the fusion of biometric properties. The objectives of the research work are defined below;
To study and explore about Iris and thumbprint recognition biometric system with their pros and cons.
To design and develop BFO for reducing the feature of iris and for thumbprint GA has been used on the basis of fitness function.
To compute the results of proposed work, using performance metrics like False Acceptance Rate, False Rejection Rate and Accuracy.
The whole proposed work “A Novel Framework for Iris and Thumbprint Recognition using Optimization Algorithms” is being done for fusion of iris and thumbprint biometrics using minutia and PCA feature extraction technique. Firstly, feature extraction using minutia for finger print and PCA for Iris is done then feature reduction is taken place using GA for IRIS and BFO optimization algorithm is used for thumbprint. The figure 3.1 depicts the flowchart of the proposed work.
Proposed work can be explained as below;
Upload iris and fingerprint …
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