This research investigates the novel techniques which provide the detailed information on the biometric images used along with the methods applied for biometric image pre-processing. It also describes the proposed methodology which was implemented with the method of optimized Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) algorithm for classification of attributes. In the current work, a big effort has been implemented for designing an efficient technique for recognizing the biometric images, especially for the modalities like finger print and retina image. Initially, the pre-processing module used the method of histogram equalization to enhance the contrasts of entire image in order to get the best image quality. This makes the image adaptable for further processing. Next, the feature extraction module has the involvement of two image sets (finger print and retina image). The Gray Level Co-occurrence Matrix (GLCM) was used for extracting the needed features in this module. Next is Feature Based Fusion Technique (FBFT) for reducing the features for authentication purpose. This research work uses the FBFT to get fused feature vector. Finally, deals with the non-recognition and recognition of the images. The images were tested by using Artificial Neural Network (ANN). Here, the recognition is done by ANN and the optimization is done by the sophisticated function of Particle Swarm Optimization Algorithm (PSOA). ANN does the classification of images as recognized and non-recognized and yields best results.
The Intelligent Computing area such as Automatic Biometric authentication is an emerging and high priority research work where the researchers invent several biometric applications which result in the revolutionary development in the recent era. In this approach, a novel algorithm is known as Modified AntLion Optimization (MALO) with Multi Kernel Support Vector Machine (MKSVM) was used to classify and recognize the fingerprint, and retina image efficiently. In the early stage of this research, the pre-processing of the biometric images was done for contrast enhancement and it was implemented by histogram equalization technique. Next, features were extracted by Gray Level Co-occurrence Matrix (GLCM), minutiae, Gray Level Run Length Matrix (GLRLM), and Autocorrelation methods. Then the features extracted were reduced by Probabilistic Principal Component Analysis (PPCA) method. Then the feature selection method was employed and the optimal features were attained by applying the Modified AntLion Optimization (MALO) technique. Finally, the machine learning classification technique was executed for categorizing biometric recognition. Here, the machine learning classification technique named Multi Kernel Support Vector Machine (MKSVM) has been used. The performance of the proposed algorithm was analyzed in terms of accuracy, sensitivity, and specificity. Results indicate that the Multi Kernel Support Vector Machine (MKSVM) yields the best accuracy of 91.60% and 90.30% for fingerprint and retina image recognition respectively, yields the sensitivity of 84.70% and 89.41% for fingerprint and retina image recognition, respectively, yields the specificity of 91.30% and 92.70% for fingerprint and retina image recognition respectively.
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