Audio- and video-based biometric person authentication

Recognition of human iris patterns for biometric identification

Electrocardiographic signal is a subject to multiple noises, caused by various factors. It is therefore a standard practice to denoise such signal before further analysis. With advances of new branch of machine learning, called deep learning, new methods are available that promises state-of-the-art performance for this task. We present a novel approach to denoise electrocardiographic signals with deep recurrent denoising neural networks. We utilize a transfer learning technique by pretraining the network using synthetic data, generated by a dynamic ECG model, and fine-tuning it with a real data. We also investigate the impact of the synthetic training data on the network performance on real signals. The proposed method was tested on a real dataset with varying amount of noise. The results indicate that four-layer deep recurrent neural network can outperform reference methods for heavily noised signal. Moreover, networks pretrained with synthetic data seem to have better results than network trained with real data only. We show that it is possible to create state-of-the art denoising neural network that, pretrained on artificial data, can perform exceptionally well on real ECG signals after proper fine-tuning.

Deep Recurrent Neural Networks for ECG Signal Denoising.

Navier-Stokes, Fluid Dynamics, and Image and Video Inpainting

In order to extract invariant features in the palmprint transformation of scale, rotation and affine distortion, a coarse-to-fine palmprint recognition method is proposed by combining the weighted adaptive center symmetric local binary pattern (WACS-LBP) and weighted sparse representation based classification (WSRC). The method consists of coarse and fine stages. In the coarse stage, using the similarity between the test sample and one sample of each training class, most of the training classes could be excluded and a small number of candidate classes of the test sample are reserved. Thus, the original classification problem becomes clear and simple. In the fine stage, the robust rotation invariant weighted histogram feature vector is extracted from each candidate sample and the test sample by WACS-LBP, and the weighted sparse representation optimal problem is constructed by the similarity between the test sample and each candidate training sample, and the test sample is recognized by the minimum residual. The proposed method is tested and compared with the existing algorithms on the PolyU and CASIA database. The experimental results illustrate better performance and rationale interpretation of the proposed method.

Combining Modified LBP and Weighted SRC for Palmprint Recognition

Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In multimodal biometric recognition, score level fusion has been a very promising approach to improve the overall system's accuracy. In this paper, score level fusion is carried out using three categories of classifiers like, rule classifier (fuzzy classifier), lazy classifier (Naive Bayes) and learning classifiers (ABC-NN). These three classifiers have their own advantages and disadvantages so the hybridization of classifiers leads to provide overall improvements. The proposed technique consists of three modules, namely processing module, classifier module and combination module. Finally, the proposed fusion method is applied to remote biometric authentication. The implementation is carried out using MATLAB and the evaluation metrics employed are False Acceptance Rate (FAR)...

Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication

The power management has become a great concern due to the increased usage of multimedia devices. Multipliers are the main sources of power consumption in these devices. The 3-2, 4-2 and 5-2 compressors are the basic components in many applications like partial product summation in multipliers. In this paper, various types of compressors have been designed. Different logic styles of XOR-XNOR gates and multiplexers have been compared with the existing CMOS logic. The pass transistor implementation of XOR-XNOR gates and multiplexer circuits achieves low power with less number of transistor counts. The proposed compressor architecture can be built using various combinations of XOR-XNOR gates, transistor level implementation and multiplexer circuits. The performance of basic compressor architectures with these low power XOR-XNOR gates and MUX blocks is found to be efficient in terms of area and power. Hence, the proposed 8x8-bit Wallace tree multiplier was designed using this proposed compressors and the power results are compared with the conventional Wallace tree multiplier design. The proposed Wallace tree multiplier using these compressors achieves significant amount of less power than conventional Wallace tree multiplier. The designs are implemented and power results are obtained using TANNER EDA 12.0 v tool.

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Design and analysis of low power compressors

An Unimodal biometric systems, which relies only on a single trait of a person for identification is often not able to meet the desired performance. Combining multiple biometrics may enhance the performance of personal authentication system in accuracy and reliability which is adopted in multimodal biometrics. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information is stored as a template which can be fused using density based score level fusion (using GMM followed by likelihood ratio test). Keywords— Biometrics, Feature, Fusion, GMM, likelihood, multimodal, Score, Template, Unimodal.

A Novel Feature Extraction Techniques for Multimodal Score Fusion Using Density Based Gaussian Mixture Model Approach

Palmprint recognition using Biometrics is one of the emerging technologies, which recognizes a person based on the principle lines, wrinkles and ridges on the surface of the palm These line structures are stable and remain unchanged throughout the life of an individual More importantly, no two palm prints from different individuals are the same, and normally people do not feel uneasy to have their palmprint images taken for testing Therefore palmprint recognition offers a promising future for medium-security access control systems The main purpose of this project is to enhance the reliability and accuracy in person identification Person identification is done by extracting the features of multispectral palmprint images in region of interest after preprocessing of multispectral palmprint images There are many filters used for feature extraction such as stack filter, curvelet filer etc, Here 2D-gabor filter is used since it is an effective tool for texture analysis and more robust to brightness The extracted feature is fused at score level and image level Finally, the Euclidean distance is used for matching of palmprint features in the database The simulated results provide high accuracy while using the fusion of multispectral palmprint images

Improvement in palmprint recognition rate using fusion of multispectral palmprint images

Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information from each modality is stored as a template. The information are fused at the match score level using a density based score level fusion, GMM followed by the Likelihood ratio test. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm.

S. Valarmathy

Papers

Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication

Design and analysis of low power compressors

A Novel Feature Extraction Techniques for Multimodal Score Fusion Using Density Based Gaussian Mixture Model Approach

Improvement in palmprint recognition rate using fusion of multispectral palmprint images

Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model