Lip biometric template security framework using spatial steganography

Pattern recognition of grooves in human lips for improved authentication in cyber-physical systems using U-Net architecture

Latent Lip groove application is been a notable topic in forensic applications like crime and other investigations. The detection of lip movement is been a challenging task since it is a smaller integral part of the human face. The conventional models operate on the available public or private dataset but it is constrained to the large population and unconstrained environment. The study aims at developing a deep learning model in a multimodal system using the deep U-Net Convolutional Neural Network architecture. It also aims at improving biometric authentication through a deep pattern recognition that involves the feature extraction of grooves present in the human lips. An examination of grooves present in the input lip image is conducted by the present system to check the authenticity of the person entering the cyber-physical systems. The lip images are collected from the public security cameras via high-definition cameras in crowded areas that help the proposed method in forensic investigation and further, it considers various unconstrained scenarios to improve the efficacy of the system. The study involves initially pre-processing of lip image, and feature extraction of lip grooves to improve the efficacy of the lip trait. The simulation is conducted on the MATLAB tool to examine the efficacy of the model against various existing methods. Further, the study does not take into account the datasets available on the websites and lip images are only collected from a large set population in a real-time environment. The results of the simulation show that the proposed method achieves a higher degree of accuracy in extracting the grooves from the input lip images.

Embedding Biometric Information in Interpolated Medical Images with a Reversible and Adaptive Strategy

How to hide messages in digital images so that messages cannot be discovered and tampered with is a compelling topic in the research area of cybersecurity. The interpolation-based reversible data hiding (RDH) scheme is especially useful for the application of medical image management. The biometric information of patients acquired by biosensors is embedded into an interpolated medical image for the purpose of authentication. The proposed scheme classifies pixel blocks into complex and smooth ones according to each block's dynamic range of pixel values. For a complex block, the minimum-neighbor (MN) interpolation followed by DIM embedding is applied, where DIM denotes the difference between the block's interpolated pixel values and the maximum pixel values. For a smooth block, the block mean (BM) interpolation is followed by a prediction error histogram (PEH) embedding and a difference expansion (DE) embedding is applied. Compared with previous methods, this adaptive strategy ensures low distortion due to embedding for smooth blocks while it provides a good payload for complex blocks. Our scheme is suitable for both medical and general images. Experimental results confirm the effectiveness of the proposed scheme. Performance comparisons with state-of-the-art schemes are also given. The peak signal to noise ratio (PSNR) of the proposed scheme is 10.32 dB higher than the relevant works in the best case.

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time-frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time-frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.