Search

Recent studies have emphasized the potential of forehead-crease patterns as an alternative for face, iris, and periocular recognition, presenting contactless and convenient solutions, particularly in situations where faces are covered by surgical masks. However, collecting forehead data presents challenges, including cost and time constraints, as developing and optimizing forehead verification methods requires a substantial number of high-quality images. To tackle these challenges, the generation of synthetic biometric data has gained traction due to its ability to protect privacy while enabling effective training of deep learning-based biometric verification methods. In this paper, we present a new framework to synthesize forehead-crease image data while maintaining important features, such as uniqueness and realism. The proposed framework consists of two main modules: a Subject-Specific Generation Module (SSGM), based on an image-to-image Brownian Bridge Diffusion Model (BBDM), which learns a one-to-many mapping between image pairs to generate identity-aware synthetic forehead creases corresponding to real subjects, and a Subject-Agnostic Generation Module (SAGM), which samples new synthetic identities with assistance from the SSGM. We evaluate the diversity and realism of the generated forehead-crease images primarily using the Fr\'echet Inception Distance (FID) and the Structural Similarity Index Measure (SSIM). In addition, we assess the utility of synthetically generated forehead-crease images using a forehead-crease verification system (FHCVS). The results indicate an improvement in the verification accuracy of the FHCVS by utilizing synthetic data., Comment: Accepted at Generative AI for Futuristic Biometrics - IJCB'24 Special Session

Biometric authentication, which utilizes contactless features, such as forehead patterns, has become increasingly important for identity verification and access management. The proposed method is based on learning a 3D spatio-spatial temporal convolution to create detailed pictures of forehead patterns. We introduce a new CNN model called the Forehead Spatio-Spatial Temporal Network (FH-SSTNet), which utilizes a 3D CNN architecture with triplet loss to capture distinguishing features. We enhance the model's discrimination capability using Arcloss in the network's head. Experimentation on the Forehead Creases version 1 (FH-V1) dataset, containing 247 unique subjects, demonstrates the superior performance of FH-SSTNet compared to existing methods and pre-trained CNNs like ResNet50, especially for forehead-based user verification. The results demonstrate the superior performance of FH-SSTNet for forehead-based user verification, confirming its effectiveness in identity authentication., Comment: 6 pages, 5 Figure, IWBF conference

In the field of neuroscience, Brain activity analysis is always considered as an important area. Schizophrenia(Sz) is a brain disorder that severely affects the thinking, behaviour, and feelings of people all around the world. Electroencephalography (EEG) is proved to be an efficient biomarker in Sz detection. EEG is a non-linear time-seriesi signal and utilizing it for investigation is rather crucial due to its non-linear structure. This paper aims to improve the performance of EEG based Sz detection using a deep learning approach. A novel hybrid deep learning model known as SzHNN (Schizophrenia Hybrid Neural Network), a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) has been proposed. CNN network is used for local feature extraction and LSTM has been utilized for classification. The proposed model has been compared with CNN only, LSTM only, and machine learning-based models. All the models have been evaluated on two different datasets wherein Dataset 1 consists of 19 subjects and Dataset 2 consists of 16 subjects. Several experiments have been conducted for the same using various parametric settings on different frequency bands and using different sets of electrodes on the scalp. Based on all the experiments, it is evident that the proposed hybrid model (SzHNN) provides the highest classification accuracy of 99.9% in comparison to other existing models. The proposed model overcomes the influence of different frequency bands and even showed a much better accuracy of 91% with only 5 electrodes. The proposed model is also evaluated on the Internet of Medical Things (IoMT) framework for smart healthcare and remote monitoring applications., Comment: 18 pages, 9 Figures

Thin films of La5/8-yPryCa3/8MnO3 (y~0.4) have been grown on single crystal SrTiO3 (001) by RF sputtering. The structural and surface characterizations confirm the epitaxial nature of these film. However, the difference between the rocking curve of the (002) and (110) peaks and the presence of pits/holes in the step-terrace type surface morphology suggests high density of defect in these films. Pronounced hysteresis between the field cool cooled (FCC) and field cooled warming (FCW) magnetization measurements suggest towards the non-ergodic magnetic state. The origin of this nonergodicity could be traced to the magnetic liquid like state arising from the delicacy of the coexisting magnetic phases, viz., ferromagnetic and antiferromagnetic-charge ordered (FM/AFM-CO). The large difference between the insulator metal transitions during cooling and warming cycles (TIMC~64 K and TIMW~123 K) could be regarded as a manifestation of the nonergodicity leading to supercooling of the magnetic liquid while cooling. The nonergodicity and supercooling are weakened by the AFM-FM phase transition induced by an external magnetic field. TIM and small polaron activation energy corresponding the magnetic liquid state (cooling cycle) vary nonlinearly with the applied magnetic field but become linear in the crystalline solid state (warming cycle). The analysis of the low temperature resistivity data shows that electron-phonon interaction is drastically reduced by the applied magnetic field. The resistivity minimum in the lower temperature region of the self-field warming curve has been explained in terms of the Kondo like scattering in the magnetically inhomogeneous regime.

The origin of the resistivity minimum observed in strongly phase separated manganites has been investigated in single crystalline thin films of LPCMO (x~0.42, y~0.40). The antiferromagnetic/charge ordered insulator (AFM/COI)-ferromagnetic metal (FMM) phase transition, coupled with the colossal hysteresis between the field cool cooled and field cooled warming magnetization demonstrates strongly phase separated nature, which gives rise to non-equilibrium magnetic liquid state that freezes into a magnetic glass. The thermal cycling and magnetic field dependence of the resistivity unambiguously shows that the pronounced resistivity minimum observed during warming is a consequence non-equilibrium states resulting from the magnetic frustration created by the delicate coexistence of the FMM and AFM/COI phases. The non-equilibrium states and hence the resistivity minimum is extremely sensitive to the relative fraction of the coexisting phases and can be tuned by intrinsic and extrinsic perturbations like the defect density, thermal cycling and magnetic field.