Laplacian Kernel Ruzicka Indexive Deep Multilayer Perceptive Network For Palm Prints Detection Classification

Palmprint is a biometric technology that involves identifying individuals based on the unique patterns and features present in their palmprints. Biometric technology is an important method to enhance security and access control through automated personal authentication. Biometric technologies includes various human traits, such as DNA, fingerprints, faces, iris patterns, palmprints, voice, signatures, and more, to perform personal authentication. Among these human traits, palmprint is an essential biometric technology, attracting significant attention in security systems. Several methods have been developed to achieve high accuracy in palmprint classification. However, challenges persist with images containing a large number of uncorrelated and redundant featuresto increase dimensionality, complicate time complexity, and reduce the accuracy.A novel method called the Laplacian Kernel Ruzicka Indexive Deep Multilayer Perceptive Network (LKRIDMPN) is introduced to enhance the accuracy of palmprint classification. The proposed palmprint identification system consists of four steps: image acquisition, preprocessing, feature extraction, and classification.During the image acquisition step, a collection of palm images is obtained from the image database. Subsequent to image acquisition, preprocessing is conducted to reduce noise and enhance image quality, thus ensuring consistency among the palm images. The Laplacian Kernel Gamma MAP filtering technique is employed to eliminate noise artifacts from the input palm image while enhancing image contrast.In the third step, the feature extraction process is carried out to minimize the time complexity of the classification process. Firstly, the Region of Interest (ROI) is extracted from the preprocessed images using the Ochiai-Barkman segmented regression method. This method helps in identifying the specific region within the image that is crucial for palmprint recognition, by assessing pixel similarity. Subsequently, a set of geometric features including principal lines, wrinkles, ridges, minutiae points, singular points, and texture features are extracted through the wavelet packet transform. These extracted features are then fed into the input layer of a deep multilayer perceptron neural network. The Ruzicka Indexive pattern matching technique is applied to analyze the extracted features with the stored templates. The Ruzicka Index score is utilized as a measure of similarity between the template and the extracted feature vector. A higher matching score indicates a stronger similarity. Classification is subsequently performed based on the matching results, leading to the palmprint recognition outcomes obtained. These results are employed to determine the individual's identity.Lastly, Powell's hybrid method is implemented to minimize the least square error, thereby enhancing the accuracy of palmprint classification at the output layer.Experimental evaluation is carried out on factors such as peak signal-to-noise ratio, palmprint detection accuracy, error rate and computational time with respect to number of images and image size.