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The proposed work investigates the performance enhancement of keystroke biometric recognition using soft biometric with filter and Score Boost Weighting (SBW) scheme. Usually, Keystroke recognition performance is lower due to user’s emotional behaviour or distraction, typing patterns vary from user normal position which causes recognition error of genuine user for degrading the recognition accuracy. To address this problem, this work presents Dual Matcher with fusion to reduce the false rejection of genuine user to improve the accuracy of keystroke recognition. In this paper, soft biometric is used as secondary information to improve the recognition accuracy for primary keystroke biometric system. Specifically, soft biometrics provides additional support for keystroke biometric recognition at the combination approach. The performance of keystroke system can be further improved using SVM as machine learning under the score level fusion in the combination approach. Lastly, the fusion technique is used to combine the primary and secondary biometric. The new approach with score fusion enhances the overall performance of keystroke biometric system with 99% accuracy. Maximum of 2% improvement is achieved compared to existing works.

In medical image processing, segmentation and classification plays a significant part in prediction of affected region from given input image. The novel segmentation and classification model to segment tumor region and to identify the abnormality category from MR brain image is projected. In preprocessing, image filtering by distribution-based adaptive median filtering technique to prove smoothness to the image and to eliminate the noise component is provided. Further the skull region is removed by using adaptive threshold-based edge detection with canny method. In the segmentation, a novel multiangle cellular automata model to predict the region of interest, i.e., tumor spot is discussed. The classification performance is improved by novel texture extraction and optimal feature selection method named as dynamic angle projection pattern and priority particle cuckoo search optimization, respectively. These optimized features are given to support vector machine (SVM) and pointing kernel classifier (PKC) to classify the abnormality level of segmented image. This work can be compared with existing systems for the parameters of sensitivity, specificity, accuracy, FPR, TPR, and ROC. Our proposed classifier PKC’s performance is good when compared with other technique. The sensitivity, specificity, and accuracy of 85.9155, 94.3396, 89.5161, and 95.7746%, 100, and 97.5806% are obtained in SVM and PKC, respectively. SVMs are linear up to 0.8 for higher values of FPR and reaches the stable point prior to the maximum value. But, the stable operation is achieved in proposed PKC at 0.9. Result shows that the improvement in performance of proposed PKC.

The performance of the wavelets within the field of image process is standard. Multiwavelets is the next step in riffle theory and it takes the performance of wavelets to the next level. In this work the performance of the Integer Multiwavelet transform (IMWT) for lossy compression has been studied. The Proposed IMWT shows sensible performance in lossy reconstruction of the images than that of Existing lossy reconstruction. This work utilizes the performance of the Proposed IMWT for lossy compression of images with encoding techniques like Magnitude set coding and Run Length Encoding. The transform coefficients are unit coded by means of Magnitude set coding and run length coding techniques which in turn results with low bits. The transform coefficient matrix is coded on not taking under consideration of the sign values using the Magnitude Set--Variable Length Integer illustration. The sign data of the coefficients is coded as bit plane with zero thresholds. This Bit plane may be used as it is or coded to scale back the bits per pixels. The Simulation was exhausted using Matlab.