Biometric Traits Research Articles

Synergistic Effects of Seed Inoculation with Piriformospora indica and Foliar Hormonal Application on Early Flowering in Rice Bean (Vigna umbellata) Variety Surabhi

This study aimed to evaluate the effectiveness of seed inoculation with Piriformospora indica and various foliar hormonal treatments in promoting early flowering in fodder rice bean (Vigna umbellata Thunb.), variety Surabhi, conducted at the College of Agriculture, Vellayani. The experiment was laid out in randomized block design with ten treatments and three replications. The plot size of 3 m2 was followed in the field experiment. The experiment assessed the impact of different treatments on flowering and other biometric traits in the leguminous crop. The treatments included untreated control, seed inoculation with Piriformospora indica before sowing and foliar applications of salicylic acid (100 ppm,150 ppm), GA₃ (200 ppm,300 ppm), Paclobutrazol (10 ppm,20 ppm) and KNO₃ (1%,1.5%) at 30 days after sowing (DAS). The results revealed that significant differences were observed among treatments for all thirteen biometric traits measured. Early flowering was recorded in plants treated with salicylic acid @ 100 ppm(65.35 days), followed by GA₃ @ 300 ppm (67.72 days), salicylic acid @ 150 ppm (67.81 days), and Paclobutrazol @ 10 ppm (69.87 days), whereas the control flowered last (80.96 days. The findings suggest that the application of specific concentrations of growth regulators and seed inoculation with P.indica can effectively reduce the time to flowering, enhancing the potential for improved seed production in fodder rice bean.

Robust Biometric Verification Using Phonocardiogram Fingerprinting and a Multilayer-Perceptron-Based Classifier

Recently, a new set of biometric traits, called medical biometrics, have been explored for human identity verification. This study introduces a novel framework for recognizing human identity through heart sound signals, commonly referred to as phonocardiograms (PCGs). The framework is built on extracting and suitably processing Mel-Frequency Cepstral Coefficients (MFCCs) from PCGs and on a classifier based on a Multilayer Perceptron (MLP) network. A large dataset containing heart sounds acquired from 206 people has been used to perform the experiments. The classifier was tuned to obtain the same false positive and false negative misclassification rates (equal error rate: EER = FPR = FNR) on chunks of audio lasting 2 s. This target has been reached, splitting the dataset into 70% and 30% training and testing non-overlapped subsets, respectively. A recurrence filter has been applied to also improve the performance of the system in the presence of noisy recordings. After the application of the filter on chunks of audio signal lasting from 2 to 22 s, the performance of the system has been evaluated in terms of recall, specificity, precision, negative predictive value, accuracy, and F1-score. All the performance metrics are higher than 97.86% with the recurrence filter applied on a window lasting 22 s and in different noise conditions.

Comprehensive evaluation of fertilizer treatments on biometric traits and marketable tuber weight across diverse potato varieties

IntroductionThe efficiency of potato production depends to a large extent on the production of quality seeds that meet the requirements of the growing region. The main component of this production includes breeding, which creates the genetic basis of seeds. The process of establishing patterns between the amount of fertilizer application (“Aquarin 12,” “Bona Forte,” “Osmocote Exact High K”), their type, and biometric characteristics of potato plants of the studied varieties “Guliver,” “Beauty of Meshchera,” and “Grand” was explored.MethodsDuring the experimental studies, the methods of variation statistics, the technique of field experiment with the corresponding transformations to achieve normal distribution, and subsequent dispersion analysis were used.ResultsThe results of the research on the evaluation of potato structural elements depending on the plant nutrition system of the studied varieties testify to the expediency of using nutrient solutions in potato cultivation irrespective of their type. In addition, it has been established that increasing the concentration of the nutrient base solution from 75 to 125% of the nutrient formula improves the quality indicators of minitubers, namely the content of starch, sugars, dry matter, crude protein, phosphorus, specific gravity, and ash content, which can contribute to obtaining high-quality potatoes.DiscussionThus, the use of morphological and physiological tools in closed ecosystems is informative for predicting key agricultural characteristics of potato plants. Additionally, integrating organic amendments and biofertilizers, as supported by other studies, could further enhance nutrient uptake and plant health, especially under conditions of low irrigation.

Multimodal biometric identification: leveraging convolutional neural network (CNN) architectures and fusion techniques with fingerprint and finger vein data

Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the “NUPT-FPV” dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity.

A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions

A robust biometric system is essential to mitigate various security threats. Electroencephalography (EEG) brain signals present a promising alternative to other biometric traits due to their sensitivity, non-duplicability, resistance to theft, and individual-specific dynamics. However, existing EEG-based biometric systems employ deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which face challenges such as high parameter complexity, limiting their practical application. Additionally, their ability to generalize across a large number of subjects remains unclear. Moreover, they have been validated on datasets collected in controlled environments, which do not accurately reflect real-world scenarios involving diverse brain conditions. To overcome these challenges, we propose a lightweight neural network model, GCT–EEGNet, which is based on the design ideas of a CNN model and incorporates an attention mechanism to pay attention to the appropriate frequency bands for extracting discriminative features relevant to the identity of a subject despite diverse brain conditions. First, a raw EEG signal is decomposed into frequency bands and then passed to GCT–EEGNet for feature extraction, which utilizes a gated channel transformation (GCT) layer to selectively emphasize informative features from the relevant frequency bands. The extracted features were used for subject recognition through a cosine similarity metric that measured the similarity between feature vectors of different EEG trials to identify individuals. The proposed method was evaluated on a large dataset comprising 263 subjects. The experimental results demonstrated that the method achieved a correct recognition rate (CRR) of 99.23% and an equal error rate (EER) of 0.0014, corroborating its robustness against different brain conditions. The proposed model maintains low parameter complexity while keeping the expressiveness of representations, even with unseen subjects.

Analysing the robustness of finger vein recognition: cross-dataset reliability and vein utility

AbstractFinger vein recognition is an emerging biometric trait known for its privacy features. Despite the remarkable performance of deep learning methods like convolutional neural networks on challenging finger vein datasets, their reliability and robustness need further examination. This study evaluates the robustness of three recognition methods—the traditional Miura Method, a supervised convolutional neural network, and an unsupervised convolutional auto-encoder—through the challenging and more realistic scenario of cross-dataset comparisons. We also analyse the reliability of these methods in terms of sample quality. We introduce a novel vein quality metric to measure vein clarity and complexity and compare it against an existing image quality metric, natural image quality evaluator. Our findings reveal differences in how these recognition methods utilise finger vein images for comparisons, highlighting the need for robust recognition techniques in more realistic scenarios. In addition, our vein quality metric effectively detects defective images, reducing the zero false-match rate from 34.98% to 8.18% on the SDUMLA-HMT dataset. These results indicate the need for metrics more focussing on finger vein image characteristics for effective quality assessment for finger vein images.

Bimodal biometric recognition system using Convolutional Neural Networks and fusion of deep audiovisual feature vectors

In recent years, interest has grown in the use biometric systems for identity authentication tasks in digital services, forensic and security applications. A unimodal system (employing a single biometric trait) with high performance is still vulnerable to falsification attacks such as spoofing. For this reason, research on multimodal biometrics (employing various biometric traits) has increased to reinforce security, increase recognition performance, and make false identity authentication more difficult. In this paper, we propose a bimodal system that combines speech and face modalities by concatenating their feature vectors, these vectors are extracted from two convolutional neural networks (CNN) and used for identity verification. The performance of unimodal CNNs was evaluated individually and compared to the bimodal system of concatenated vectors. A data augmentation scheme is used for both modalities to evaluate different operation conditions. Results were measured in terms of Equal Error Rate (EER).

PERFORMANCE EVALUATION OF SUGARCANE CLONES AT PRELIMINARY YIELD TRIAL

In the current study, fifteen (15) sugarcane clones developed from Sri Lanka and Barbados fuzz were evaluated for quantitative and qualitative traits in local set of climatic conditions. After testing and advancement from early stages of sugarcane cultivar development program, these clones were planted in 4th stage i.e. preliminary yield trial to evaluate the performance of clones in comparison to two local check varieties viz. CPF 253 and HSF 240. The testing sugarcane clones along with check varieties were planted in a randomized complete block design (RCBD) deploying three replications during the crop season 2022-23. Biometric parameters like germination (%), plant population, plant height, 10-cane weight, cane weight and cane yield alongwith sugar recovery were studied. The biometric traits were compared using two-sided Dunnett's multiple to check the level of significance. The results showed high level of significance for all the biometric traits. The clone BDF-20-174 showed superior performance for plant population, cane yield and sugar recovery as compared to local check varieties. Similarly some other clones surpass the check varieties in some traits but their overall performance remained at par or lower than check varieties. However, further testing and experimentation will be applied to pick more adapted and suitable clones for local agro-ecological conditions

Improving Beneficial Traits in Bacillus cabrialesii subsp. cabrialesii TE3T through UV-Induced Genomic Changes.

It is essential to hunt for new technologies that promote sustainable practices for agroecosystems; thus, the bioprospecting of beneficial microorganisms complementing with mutation induction techniques to improve their genomic, metabolic, and functional traits is a promising strategy for the development of sustainable microbial inoculants. Bacillus cabrialesii subsp. cabrialesii strain TE3T, a previously recognized plant growth-promoting and biological control agent, was subjected to UV mutation induction to improve these agro-biotechnological traits. Dilutions were made which were spread on Petri dishes and placed under a 20 W UV lamp at 10-min intervals for 60 min. After the UV-induced mutation of this strain, 27 bacterial colonies showed morphological differences compared to the wild-type strain; however, only a strain named TE3T-UV25 showed an improvement in 53.6% of the biocontrol against Bipolaris sorokiniana vs. the wild-type strain, by competition of nutrient and space (only detected in the mutant strain), as well as diffusible metabolites. Furthermore, the ability to promote wheat growth was evaluated by carrying out experiments under specific greenhouse conditions, considering un-inoculated, strain TE3T, and strain TE3T-UV25 treatments. Thus, after 120 days, biometric traits in seedlings were quantified and statistical analyses were performed, which showed that strain TE3T-UV25 maintained its ability to promote wheat growth in comparison with the wild-type strain. On the other hand, using bioinformatics tools such as ANI, GGDC, and TYGS, the Overall Genome Relatedness Index (OGRI) and phylogenomic relationship of mutant strain TE3T-UV25 were performed, confirming that it changed its taxonomic affiliation from B. cabrialesii subsp. cabrialesii to Bacillus subtilis. In addition, genome analysis showed that the mutant, wild-type, and B. subtilis strains shared 3654 orthologous genes; however, a higher number of shared genes (3954) was found between the TE3T-UV25 mutant strain and B. subtilis 168, while the mutant strain shared 3703 genes with the wild-type strain. Genome mining was carried out using the AntiSMASH v7.0 web server and showed that mutant and wild-type strains shared six biosynthetic gene clusters associated with biocontrol but additionally, pulcherriminic acid cluster only was detected in the genome of the mutant strain and Rhizocticin A was exclusively detected in the genome of the wild-type strain. Finally, using the PlaBase tool, differences in the number of genes (17) associated with beneficial functions in agroecosystems were detected in the genome of the mutant vs. wild-type strain, such as biofertilization, bioremediation, colonizing plant system, competitive exclusion, phytohormone, plant immune response stimulation, putative functions, stress control, and biocontrol. Thus, the UV-induced mutation was a successful strategy to improve the bioactivity of B. cabrialesii subsp. cabrialesii TE3T related to the agro-biotecnology applications. The obtained mutant strain, B. subtilis TE3T-UV25, is a promising strain to be further studied as an active ingredient for the bioformulation of bacterial inoculants to migrate sustainable agriculture.

Multimodal Biometric System Fusion Using Fingerprint and Iris with Convolutional Neural Network

Biometric sensing technology became everyday life frequent component as a result of world requirement for info security and safety legislation. A strong and efficient individual authentication has appeared because of new developments in multimodal biometrics. Multimodal biometrics integrates different biological traits in trying for creating considerable effect on identification performance. Latent fingerprint biometrics refer to effective human identification system for criminals given the accessible crime evidence shreds. Although, biometric trait restrictions like intra-class variation, sensed data noise, lack of individuality caused low matching score that possesses a negative effect on recognition and investigation process. This paper uses two unimodal biometrics—the fingerprint and the iris—applied as multi-biometrics to show that using these biometrics can produce excellent results with high accuracy. Every biometric result is weighted for involvement in the final decision, and the decision level is utilized for fusion. For every biometric result integration effect, a neural network is used. The datasets' experimental findings have demonstrated a notable biometric system identification capability. The accuracy performance of the suggested approach is 100, the FAR is 0.1, and the EER is 0.1. To demonstrate the efficacy of the suggested system, the suggested method is contrasted with a few other approaches currently in use.

Significance of reeling waste water on the biometric traits of mulberry (Morus Sp.)

The present study was conducted in Department of sericulture, Forest college and research institute, Mettupalayam. In the study, we analysed the impact of reeling waste water on the plant propagation and growth of mulberry cuttings, reeling water samples were collected from reeling units in vadavalli, coimabtore district. Cuttings of V1 and G4 were selected and maintained in a pot culture, frequently irrigated with reeling water at different concentrations. Observations pertained to treatments were calculated at 30,60 and 90 days after planting, from that recordings, present investigation was concluded that the sprouting and other studied biometric traits except internodal distance in both V1 and G4 shows higher value at lowest concentration of reeling waste water i.e., T2 (25% reeling waste water + 75% tap water). Highest concentration of reeling waste water reduces the sprouting as well as growth of the mulberry cuttings due to the presence of more amount of elements which affects the metabolic activities of the plants. Except T5 (100% reeling waste water) all other concentrations were exhibited better propagation and growth of mulberry cuttings.

Mathematical Modelling for Neural Network-Based Textural Feature Extraction from Normalized Ocular Images for Improved Biometric Authentication

Biometric authentication systems have become increasingly prevalent in security and identity verification applications. Among various biometric traits, the ocular region, specifically the iris, offers a unique and stable source for individual identification. The intricate patterns within the iris are highly distinctive and remain consistent over a person's lifetime, making them ideal for biometric applications. In the field of biometric authentication, the ocular region has emerged as a robust and reliable source for identity verification due to its unique and stable features. This study focuses on enhancing the accuracy of biometric systems through neural network-based textural feature extraction from normalized ocular images. The proposed approach utilizes Mask Image Extraction (Hough circle) and an efficient U-Net model to achieve superior performance in biometric authentication. Initially, the ocular images are pre-processed to normalize lighting and orientation, ensuring consistency across the dataset. Mask Image Extraction, particularly using the Hough circle transform, is employed to isolate the region of interest (ROI) within the ocular images. This method accurately detects and segments the circular iris region, which is crucial for subsequent feature extraction. The core of the proposed system is the Efficient U-Net model, a variant of the standard U-Net architecture designed for enhanced efficiency and accuracy. The U-Net model is trained to extract intricate textural features from the segmented ocular regions. By leveraging its encoder-decoder structure, the U-Net captures both local and global features, resulting in a detailed and discriminative representation of the ocular texture. Experimental results demonstrate that the integration of Mask Image Extraction and the Efficient U-Net model significantly improves the accuracy of biometric authentication systems. The proposed method outperforms traditional approaches by providing more precise and reliable feature extraction, thereby enhancing the overall robustness and reliability of ocular-based biometric systems.

Personal identification system based on multidimensional electroencephalographic signals

Personal authentication using electroencephalographic (EEG) signals, is one of the important applications in brain computer interface (BCI). In this work we investigate the use of EEG signals as a biometric trait. Multidimensional EEG signals were represented as symmetric positive-definite (SPD) matrices on a Riemannian manifold. Two experiments are performed in the first; we use minimum distance to Riemannian mean (MDRM) as a classifier. In the second; SPD matrices are vectorized, and the generated vectors are used to train various machine learning (ML) classifiers. MDRM classifier achieved a correct recognition rate (CRR) of 96.92% , while ML classifiers achieved CRR from 95.39% to 99.45%.

Effects of maternal androgens and their metabolite etiocholanolone on prenatal development in birds.

Offspring phenotypes can be affected by maternal testosterone and androstenedione (A4), which are considered a tool of mothers to adjust offspring to a fluctuating environment. Yet testosterone and A4 are very rapidly metabolized by developing avian embryos, suggesting that either the maternal testosterone and A4 have potent organizational effects on the embryos extremely early before being metabolized or it is the metabolites that evoke phenotypic variation in the offspring. One of the metabolites, etiocholanolone, increases substantially during early embryonic development and is a likely candidate for mediating maternal effects as it can promote erythropoiesis. To investigate and compare the effects of testosterone and A4 with the possible effects of etiocholanolone during prenatal embryonic development, we increased their levels in black-headed gull eggs (Larus ridibundus), and used sham-injected eggs as controls. This species usually has 3-egg clutches in which maternal androgen levels increase with the egg-laying sequence. We analysed embryonic heart rate, peri-hatching biometric traits, the ratio of white to red blood cells (W/R ratio) and bursa development. We found that testosterone and A4 treatment increased embryonic heart rate irrespective of egg-laying sequence and decreased bill length and W/R ratio, whereas etiocholanolone did not mimic these effects. Instead, etiocholanolone treatment decreased tarsus length and brain mass. Our finding that etiocholanolone does not mimic the effects induced by testosterone and A4 suggests that the embryonic metabolism of maternal testosterone and A4 can potentially diversify the function of these maternal androgens.

HCFAIUN: A novel hyperelliptic curve and fuzzy extractor-based authentication for secure data transmission in IoT-based UAV networks

IoT-based UAV networks comprise interconnected UAVs outfitted with sensors and microcontrollers to simplify data exchange in environments such as smart cities. In light of open-access communication landscapes, IoT-based UAV networks could pose security challenges, encompassing authentication vulnerabilities and the inadvertent disclosure of location and other confidential information to unauthorised parties. Henceforth, we have proposed a lightweight and secure authentication protocol: Hyperelliptic Curve and Fuzzy Extractor based Authentication in IoT-based UAV networks (HCFAIUN) leveraging Hyperelliptic Curve Cryptography(HCC), Fuzzy Extractor (FE), XOR operations and hash functions. HCC's maximum key size is 80 bits, differing from the 160-bit requirement of the elliptic curve, making it apt for UAVs with limited resources. The proposed scheme utilises biometrics traits of users to avoid exposing data from stealing smart devices using FE. This protocol facilitates the mutual authentication of users and UAVs, allowing them to exchange a session key for secure communication. The Hyperelliptic Curve (HC) scalar multiplication protects the user's private key from attackers, even in public channels. The obfuscation identity of the user and UAVs generated through the hash function and timestamp makes the external user and UAV anonymous. The efficacy of this proposed framework is examined using the Scyther verification tool and Random oracle model-based formal analysis, and informal analysis is also discussed, which validates its robustness against well-known potential physical and logical attacks. The performance analysis shows that the HCFAIUN scheme has lower computation, communication, and storage costs, i.e., 3.832 ms and 1456 bits and 1128 bits, respectively, compared to existing schemes.

Phyllosphere Microbiome in Ecosystem Management and Plant Growth Promotion for Agricultural Sustainability

This review paper attempts to interpret the various role of phyllosphere microbiome, survival, resistant mechanisms to confront the adverse environmental conditions, production of pigment, extracellular polysaccharides, biosurfactants to promote surface attachment desiccation protection, volatile organic compounds, phytoalexins, as a defensive role to compete for space and nutrients. The beneficial phyllosphere microbes contributing to promotion of plant growth by facilitating nutrient acquisition, modulating hormonal signaling, biocontrol for plant pathogens in multi-various crops is discussed in detail. Phyllobacteria show encouraging interactions with host plants in improving plant health and biometric traits by regulating nutrient acquisition, phytohormones production, biotic and abiotic stress management. The members of genera Bacillus, Enterobacter, Microbacterium, Methylobacterium, Stenotrophomonas, Pseudomonas, Pseudarthrobacter, and Kocuria were the most dominant plant growth promoting bacteria reported in phyllosphere. The beneficial phyllosphere microorganism enhances crop yield while reducing the environmental footprint associated with synthetic fertilizers and pesticides. The review focuses and thrust on the development as well as commercialization of biostimulants derived from phyllosphere microbiomes. These biostimulants, and their metabolites, can be tapped to enhance plant growth, nutrient uptake, and overall crop performance in organic farming. Furthermore, this review paper focus on certain drawbacks that there have been few researches on phyllosphere yeast and its symbiotic association with bacteria, thereby emphasizing subsequent research in this area.

Multispectral, Thermographic and Spectroradiometric Analyses Unravel Bio-Stimulatory Effects of Wood Distillate in Field-Grown Chickpea (Cicer arietinum L.)

Wood distillate (WD) has recently emerged as a promising bio-stimulant for sustainable legume crop production, owing to its ability to enhance seed yield and quality. However, no studies exist on the effects of WD on chickpea plants at pre-harvesting stages, hindering the farmers’ ability to acquire valuable knowledge on the early action of WD on the plants’ status and preventing the establishment of proactive measures to optimize WD use in agriculture. In this study, two multispectral, thermographic and spectroradiometric surveys, along with in-situ measurements of specific plant biometric traits, were conducted across the reproductive stage of field-grown chickpea in order to evaluate the early involvement of WD on plant health. The acquired multispectral images were used to calculate the Normalized Difference Vegetation Index (NDVI), revealing a notable ~35% increase in NDVI scores of WD-treated plants at the onset of physiological maturity, and indicating an improved plant status compared to the control (water-treated) plants. Moreover, control and WD-treated plants exhibited distinct spectral signatures across the visible, near-infrared (NIR) and short-wave infrared (SWIR) spectra, suggesting potential changes in their photosynthetic capacity, structural properties and water content both at the leaf and at the pod level. Furthermore, WD-treated plants showed a 25% increase in pod production, particularly at the beginning of seed maturity, suggesting that enhancements in plant status were also reflected in higher pod yields. These results point to a beneficial effect of WD on plant health during the preliminary stages of seed formation and indicate that a combination of both multispectral and spectroradiometric analyses can provide critical insights on the status of chickpea crops at pre-harvesting stages. In addition, these findings emphasize the importance of analyzing pre-harvesting stages to gain insights into the early involvement of WD in promoting plant health and, ultimately, in predicting final crop yields.

Biometric variability of inflorescence and flower traits among ex situ accessions of the neotropical oilseed palm Acrocomia Mart.

The oilseed palm genus Acrocomia is suitable for sustainable oil production in South America. The high phenotypic diversity of wild populations poses a challenge for the delimitation of the genus. Comparing the inflorescence architecture, a first-order panicle, and staminate and pistillate flower traits could be a valuable tool in resolving the taxonomic disarray. Thus, this study aims to characterize the differences in the inflorescence architecture and floral structures of three common and economically significant Acrocomia species: A. aculeata, A. totai, and A. intumescens. Biometric traits of the inflorescence architecture and floral structures of various Acrocomia accessions in an ex situ germplasm collection in Brazil were assessed. The unweighted pair group method with arithmetic mean (UPGMA) cluster analysis based on the Gower distance was used to measure dissimilarities between the individual plants of the accessions. To our best knowledge, this study provides the first evidence of the presence of second-order rachillae in the genus Acrocomia. Evaluated traits showed a high level of variation within and between accessions, emphasizing the phenotypic diversity of the genus. The accessions of A. totai were distinguishable from those of the other two species by their inflorescence architecture and flower traits. The dissimilarities between A. aculeata and A. intumescens were not sufficient to differentiate both. In conclusion, the quantitative assessment of inflorescence and floral traits is a valuable tool for taxonomic resolution of the genus.

An Adaptive Approach of Face Recognition for Low Intensity Images Using Texture Features

Abstract: Biometric recognition is the major field in the process of digitalization due to security. Face is an important biometric trait that can be used for security and authorization process. In this paper a novel approach has been proposed for face recognition process. Face recognition has major drawback of accuracy under low quality images that has improved in proposed approach. In this paper an approach of texture feature extraction has been hybrid with Dual cross pattern approach so that optimum set of feature can be computed. Experimental results have been extracted under variation in resolution. Proposed approach outperform to existing approaches.

Security Scheme by Using Steganography in Multi-ModelBiometric System

The process of automatically identifying a person based on their physiological and behavioural traits is known as biometric recognition. A unimodal or multimodal biometric system might be based on physiological or behavioural traits. Some shortcomings in the unimodal biometric system include spoofing, non- universality, intra-class differences, and noisy data. Using the face and palm images as templates, we are implementing a multimodal biometric system. A person's palm image can provide a wealth of detailed information about them. Palm image have three major lines, name as principle line, secondary line and wrinkles which are unique in nature. Face image have more feature than any other biometric traits. Face image is recognized easily in less time. Combining these two biometric template gives higher security. Message image kept secretly in the cover image called stego image. In this steganography technique we are using palm image as a Message and face image as a cover image. After getting stego image Identification process works. The important featureof identification process id De-Stego. This feature of identification will separate the message image (palm image) from cover image (face image). After separation of images the decision will take care, weather the image identified or notidentified.