A Robust Multi-Modal Biometric Recognition System Using Iris, Fingerprint and Palmprint based on Cuckoo Search Algorithm
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Abstract: Authentication enables individuals to be automatically recognized based on their behavioral or physiological traits. Biometrics is extensively utilized in many commercial and official identifying systems to facilitate automated access control. This research presents a model for multimodal biometric recognition that utilizes a feature level fusion method. The suggested method encompasses a series of five processes, namely pre-processing, feature extraction from all attributes, feature level fusion, feature space reduction, and recognition via machine learning techniques. The initial stage involves the pre-processing of three distinct modalities, namely iris, pamprint, and fingerprint. Next, the process of feature extraction is conducted for each modality in order to extract the features. Following this, the features extracted from three modalities were combined at the feature level. The utilization of feature level fusion in integrating multiple biometric data presents several advantages in comparison to alternative fusion procedures, but accompanied by the notable limitation of creating feature vectors of substantial dimensions. The main objective of this study is to analyze the difficulties related to the management of high-dimensional data and investigate several methods of feature reduction that can be applied to multimodal biometric systems.
This study presents a novel approach that employs Cuckoo Search (CS) optimization technique for the purpose of feature selection. The objective is to address the challenges related to integrating the Iris, palmprint, and fingerprint feature spaces at the feature level. Normalization is applied to bring all the feature spaces into same domain during integration of features at feature level. Machine Learning approaches are utilized to assess the effectiveness of feature selection based on Cuckoo Search Algorithm (CSA) and feature space reduction using Principal Component Analysis (PCA) on the CASIA, IITD, and FVC databases. Additionally, matching is performed using the Euclidean distance. The trials undertaken in this study indicated a significant reduction in the feature space when iris, palmprint, and fingerprint characteristics were merged at the feature level. Specifically, the use of CS resulted in a greater reduction compared to PCA. The decrease in size led to an improvement in the accuracy of recognition.
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