Deep learning approach for Touchless Palmprint Recognition based on Alexnet and Fuzzy Support Vector Machine

Due to stable and discriminative features, palmprint-based biometrics has been gaining popularity in recent years. Most of the traditional palmprint recognition systems are designed with a group of hand-crafted features that ignores some additional features. For tackling the problem described above, a Convolution Neural Network (CNN) model inspired by Alex-net that learns the features from the ROI images and classifies using a fuzzy support vector machine is proposed. The output of the CNN is fed as input to the fuzzy Support vector machine. The CNN\u27s receptive field aids in extracting the most discriminative features from the palmprint images, and Fuzzy SVM results in a robust classification. The experiments are conducted on popular contactless datasets such as IITD, POLYU2, Tongji, and CASIA databases. Results demonstrate our approach outperformers several state-of-art techniques for palmprint recognition. Using this approach, we obtain 99.98% testing accuracy for the Tongji dataset and 99.76 % for the POLYU-II datasets

3D Face Recognition using Significant Point based SULD Descriptor

In this work, we present a new 3D face recognition method based on Speeded-Up Local Descriptor (SULD) of significant points extracted from the range images of faces. The proposed model consists of a method for extracting distinctive invariant features from range images of faces that can be used to perform reliable matching between different poses of range images of faces. For a given 3D face scan, range images are computed and the potential interest points are identified by searching at all scales. Based on the stability of the interest point, significant points are extracted. For each significant point we compute the SULD descriptor which consists of vector made of values from the convolved Haar wavelet responses located on concentric circles centred on the significant point, and where the amount of Gaussian smoothing is proportional to the radii of the circles. Experimental results show that the newly proposed method provides higher recognition rate compared to other existing contemporary models developed for 3D face recognition

RGB-D FACE RECOGNITION USING LBP-DCT ALGORITHM

Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment

WEBER'S LOCAL DESCRIPTOR AND DIFFERENTIAL EXCITATION DIFFERENCE BASED TEXT DETECTION TECHNIQUE: AN INTEGRATED APPROACH

In this paper, we have proposed a robust and an efficient text detector in a video frame. The proposed model is based on Weber’s excitation principle and difference of excitation information is used to highlight the textual data in a video frame. In order to exhibit the performance of the proposed model, we have conducted experimentation on the standard ICDAR-2003 dataset and our own video database. Experimental comparison and objective comparison is also provided with the recently proposed models

Skin Colour Information and Morphology Based Face Detection Technique

Locating and tracking human faces is a prerequisite for face recognition and/or facial\ud expressions analysis, although it is often assumed that a normalized face image is available. In\ud this paper, we propose a faster, yet efficient face detection approach based on mathematical\ud morphology and skin colour information. We have devised some simple post-processing rules to\ud eliminate non-face regions from face regions. Experimentation on our created database is\ud conducted to reveal the performance of the proposed approach

Clinical Characterization and Genomic Analysis of Samples from COVID-19 Breakthrough Infections during the Second Wave among the Various States of India

From March to June 2021, India experienced a deadly second wave of COVID-19, with an increased number of post-vaccination breakthrough infections reported across the country. To understand the possible reason for these breakthroughs, we collected 677 clinical samples (throat swab/nasal swabs) of individuals from 17 states/Union Territories of the country who had received two doses (n = 592) and one dose (n = 85) of vaccines and tested positive for COVID-19. These cases were telephonically interviewed and clinical data were analyzed. A total of 511 SARS-CoV-2 genomes were recovered with genome coverage of higher than 98% from both groups. Analysis of both groups determined that 86.69% (n = 443) of them belonged to the Delta variant, along with Alpha, Kappa, Delta AY.1, and Delta AY.2. The Delta variant clustered into four distinct sub-lineages. Sub-lineage I had mutations in ORF1ab A1306S, P2046L, P2287S, V2930L, T3255I, T3446A, G5063S, P5401L, and A6319V, and in N G215C; Sub-lineage II had mutations in ORF1ab P309L, A3209V, V3718A, G5063S, P5401L, and ORF7a L116F; Sub-lineage III had mutations in ORF1ab A3209V, V3718A, T3750I, G5063S, and P5401L and in spike A222V; Sub-lineage IV had mutations in ORF1ab P309L, D2980N, and F3138S and spike K77T. This study indicates that majority of the breakthrough COVID-19 clinical cases were infected with the Delta variant, and only 9.8% cases required hospitalization, while fatality was observed in only 0.4% cases. This clearly suggests that the vaccination does provide reduction in hospital admission and mortality