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Face Detection-Based Border Security System Using Haar-Cascade and LBPH Algorithm
Border security is a process which measures the border management ideas by a country or set of countries to wage against unspecific and unauthorized travel or trade across the country borders, to bound non-legal deport, various crimes combat, and foreclose dangerous criminals from entering in the country. A system will help in keeping a check on those personnels who forge with the legal document with an intension to cross the border. This article discusses about border security of whole Indian context, and there are various such systems which have been built since 2010 as wireless sensor network system named Panchendriya. Remote and instruction manual switch mode arm system using ultrasonic sensor for security of border. This article we have made use of Haar-Cascadian along with LBPH algorithm with their functioning. The result and discussion section we compare most recent face recognition techniques that have been used in the last ten years. The proposed prototype is discussed and shown through simulation model, it provide better result compare to existing model. The proposed Haar-Cascade and LBPH algorithm provide 10% better performance. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Face-Based Kinship Verification using Deep Embeddings for Low-Cost Health Record Linkage
Precise linkage of health records is essential for continuity of care, reducing duplicate health records, and accurately documenting family medical histories. Genomic testing offers the evidence-based biological 'gold standard' for verifying kinship; however, access to testing is either impossible or unavailable in most low-resourced environments due to prohibitive costs, long timelines, and/or lack of infrastructure. This study provides a low cost and interpretable pipeline for kinship verification in the form of Siamese deep embeddings. The processed facial image embeddings produced by a ResNet-18 backbone using 256-dimensional and L2-normalized embeddings, are then compared using cosine similarity. A validation-based calibration process selects the logit polarity and decision threshold that support stable deployment decisions. Grad-CAM visualizations can be interpreted frame-by-frame and allow for pair-specific attributions of faces that were more relevant or important in decisions of similarity. In experiments on the Families in the Wild (FIW) dataset (family-disjoint splits), we report ROC-AUC of 0.834, target balanced accuracy of ?0.88, with similar precision, recall, and specificity. The confusion matrices also illustrate a near symmetric distribution of errors by family and both Grad-CAM explanations highlight how the model came to a decision for true cases and hard cases. The above results illustrate how we can deploy a lightweight, explainable, and face-based kinship verification pipeline on a CPU-only system. Our study therefore provides a feasible assistive tool for health record linkage where genomic validation is not possible. 2025 IEEE. -
Facebook as a Socialising Agent and its Impact on Academic Achievements on an Individual
Golden Research Thoughts Vol.2,Issue 9,pp.1-3 ISSN No. 2231-5063 -
Facebook post analysis for a celebrity page
Social Media is a subject that is large and extremely talked about in the present day. The expanding accessibility of the web and the development of social media brought in a necessity for the analysis of data based on a Facebook page. The number of information generated by social media platforms are expanding exponentially. The best approach to understand data of this magnitude lies in mining and analysis of such big data. The present focus is on the analysis of a Facebook Fan Page. This research work concentrates on the examination of the number of likes, post shares and correlation of posts and comments on various topics, fields or areas of a Facebook page. 'Like' is not the only way of rating a post nowadays. There are also recently added features like emoticons which display emotions such as 'love', 'wow', 'angry', 'sad', etc., to rate a post. The objective of this paper is to extract the data available and to perform analysis on a cricket fan page. Python is used to extract and analyse the information. IAEME Publication. -
Facial Emotion Detection Using Deep Learning: A Survey
The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE. -
Facial Emotion Recognition Augmented with CNNs and Face Detection: Toward Emotive Emoji Synthesis
Emotion recognition is a crucial component with broad applications in technology and healthcare industries specifically in humancomputer interaction. To improve emotion recognition accuracy, this research introduces an innovative technique that integrates face detection with Convolutional Neural Networks (CNNs). Using the Fer2013 dataset, the approach consists of carefully identifying faces in images as a preprocessing step, followed by training a CNN network to identify emotions and create corresponding emojis. After conducting extensive testing and assessment, it is determined that after employing a face detection algorithm the suggested framework is effective in both correctly identifying emotions and producing visually appealing emojis. This helps to create an interface for emotional communication that is more user-friendly and captivating. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Facial emotion recognition using convolutional neural networks
Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FERC). The FERC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (EV) is utilised in the FERC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FERC varies from widely utilised CNN single-level technology. Moreover, EV generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera). 2021 -
Facial Expression Recognition Using Pre-trained Architectures
In the area of computer vision, one of the most difficult and challenging tasks is facial emotion recognition. Facial expression recognition (FER) stands out as a pivotal focus within computer vision research, with applications in various domains such as emotion analysis, mental health assessment, and humancomputer interaction. In this study, we explore the effectiveness of ensemble methods that combine pre-trained deep learning architectures, specifically AlexNet, ResNet50, and Inception V3, to enhance FER performance on the FER2013 dataset. The results from this study offer insights into the potential advantages of ensemble-based approaches for FER, demonstrating that combining pre-trained architectures can yield superior recognition outcomes. 2024 by the authors. -
Facial Expression Recognition with Transfer Learning: A Deep Dive
In the realm of affective computing, where the nuanced interpretation of facial expressions plays a pivotal role, this research presents a comprehensive methodology aimed at refining the precision of facial expression recognition on the CK+ (Cohn-Kanade Extended) dataset. Our method uses the robust DenseNet121 architecture that has been pretrained on the 'imagenet' dataset, and it leverages transfer learning on the foundational CK+ dataset. The model deftly handles issues with overfitting, normalization, and feature extraction that are present in facial expression detection on CK+. Our approach not only achieves an overall accuracy of 98%, with a 5.86% accuracy enhancement over the base paper on the CK+ dataset, but also shows remarkable precision, recall, and F1-score values for individual emotion classes. It is noteworthy that emotions such as anger, contempt, and disgust have precision rates that reach 100%. The study highlights the model's noteworthy input to affective computing and presents its possible real-world uses in emotion analysis on CK+ and human-computer interaction. 2024 IEEE. -
Facial pain expression recognition in real-time videos
Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%-55% and an improved processing time of 20%-25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%-100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC-McMaster Shoulder Pain Database as well. 2018 Pranti Dutta and Nachamai M. -
Facial Recognition Model Using Custom Designed Deep Learning Architecture
Facial Recognition is widely used in some applications such as attendance tracking, phone unlocking, and security systems. An extensive study of methodologies and techniques used in face recognition systems has already been suggested, but it doesn't remain easy in the real-world domain. Preprocessing steps are mentioned in this, including data collection, normalization, and feature extraction. Different classification algorithms such as Support Vector Machines (SVM), Nae Bayes, and Convolutional Neural Networks (CNN) are examined deeply, along with their implementation in different research studies. Moreover, encryption schemes and custom-designed deep learning architecture, particularly designed for face recognition, are also covered. A methodology involving training data preprocessing, dimensionality reduction using Principal Component Analysis, and training multiple classifiers is further proposed in this paper. It has been analyzed that a recognition accuracy of 91% is achieved after thorough experimentation. The performance of the trained models on the test dataset is evaluated using metrics such as accuracy and confusion matrix. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Facile combustion synthesis of highly active Mo doped BiVO4 for photocatalytic dye degradation, photo-oxidation of alcohols, antifungal and antioxidant activities
This work represents the facile and green synthesis of Molybdenum (Mo)-doped bismuth vanadate (BiVO4). Green synthesis of Mo-doped BiVO4 was done using combustion technique using Mangifera indica (Mango) leaf extract as the fuel for combustion. The material synthesised was pure and characterised using X-ray diffraction, scanning electron microscopy, high resolution transmission electron microscopy, ultravioletvisible diffuse reflectance spectroscopy, Fourier transform infrared spectroscopy and photoluminescence (PL). It was found that Mo-doped BiVO4 had monoclinic scheelite phase, with a bandgap of 3.71eV. Various application was possible from the synthesised material like photodegradation of Malachite Green, a typical organic which showed excellent degradation efficiency of 99% under 120minutes. The catalyst also gave up to 95% yields in the light-assisted oxidation of aromatic alcohols to corresponding aldehydes. The material also showed excellent antioxidant properties showing 6.7g of ascorbic acid equivalence (AAE). It gave an excellent minimum lethal dosage (MLD) of 500g against Penicillium and Trichoderma fungal strains and showed maximum of 32 mm zone of inhibition. These applications show the versatility of the material to be used in various fields. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Facile construction of gefitinib-loaded zeolitic imidazolate framework nanocomposites for the treatment of different lung cancer cells
Gefitinib (GET) is a revolutionary targeted treatment inhibiting the epidermal growth factor receptor's tyrosine kinase action by competitively inhibiting the ATP binding site. In preclinical trials, several lung cancer cell lines and xenografts have demonstrated potential activity with GET. Response rates neared 25% in preclinical trials for non-small cell lung cancer. Here, we describe the one-pot synthesis of GET@ZIF-8 nanocomposites (NCs) in pure water, encapsulating zeolitic imidazolate framework 8 (ZIF-8). This method developed NCs with consistent morphology and a loading efficiency of 9%, resulting in a loading capacity of 20wt%. Cell proliferation assay assessed the anticancer effect of GET@ZIF-8 NCs on A549 and H1299 cells. The different biochemical staining (Calcein-AM and PI and 4?,6-Diamidino-2-phenylindole nuclear staining) assays assessed the cell death and morphological examination. Additionally, the mode of apoptosis was evaluated by mitochondrial membrane potential (??m) and reactive oxygen species. Therefore, the study concludes that GET@ZIF-8 NCs are pledged to treat lung cancer cells. 2024 International Union of Biochemistry and Molecular Biology, Inc. -
Facile engineering of aptamer-coupled silk fibroin encapsulated myogenic gold nanocomposites: investigation of antiproliferative activity and apoptosis induction
Nanocomposites selectively induce cancer cell death, holding potential for precise liver cancer treatment breakthroughs. This study assessed the cytotoxicity of gold nanocomposites (Au NCs) enclosed within silk fibroin (SF), aptamer (Ap), and the myogenic Talaromyces purpureogenus (TP) against a human liver cancer cell (HepG2). The ultimate product, Ap-SF-TP@Au NCs, results from a three-step process. This process involves the myogenic synthesis of TP@Au NCs derived from TP mycelial extract, encapsulation of SF on TP@Au NCs (SF-TP@Au NCs), and the conjugation of Ap within SF-TP@Au NCs. The synthesized NCs are analyzed by various characteristic techniques. Ap-SF-TP@Au NCs induced potential cell death in HepG2 cells but exhibited no cytotoxicity in non-cancerous cells (NIH3T3). The morphological changes in cells were examined through various biochemical staining methods. Thus, Ap-SF-TP@Au NCs emerge as a promising nanocomposite for treating diverse cancer cells. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Facile fabrication of 3D-?-Fe2O3@2D-g-C3N4 heterojunction composite materials: Effect of iron oxide loading on the electrochemical performance
Designing heterojunction nanocomposites is crucial for optimizing supercapacitor electrodes. This study addresses the challenge by introducing a facile synthesis method for creating 3D-?-Fe2O3@2D-g-C3N4 heterojunctions through a bulk carbon nitride-assisted hydrothermal process. During this process, the growth of ferric oxide particles coincides with the exfoliation and deposition of carbon nitride, leading to simultaneous structural changes in both iron oxide and carbon nitride phases. The resulting composite's properties strongly correlate with the iron oxide loading. Comprehensive characterization using XRD, FTIR, SEM-EDAX, XPS and TEM identified three distinct structures for ?-Fe2O3/g-C3N4 composites based on iron oxide loading: low, medium, and high. The medium-loaded sample demonstrated superior electrochemical performance, attributed to better interfacial contact and the formation of 3D-Fe2O3@2D-g-C3N4 heterojunctions. This composite, with an optimized 22 wt% iron oxide loading, exhibited a maximum specific capacitance of 925.1 Fg?1 at 5 mVs?1 and 498.6 Fg?1 at 6 Ag?1 in charge-discharge analysis, with stable performance over 2000 cycles. Overall, this research presents an enhanced hydrothermal method for facile preparation of effective ?-Fe2O3/g-C3N4 heterojunction materials. 2024 Elsevier B.V. -
Facile fabrication of dasatinib laden multifunctional polymeric micelles: Evaluation of anti-proliferative and apoptotic activities in human cancer cells
Dasatinib (DAS) has recently gained significant interest for its anticancer potential. Yet, the lipophilicity inherent in DAS limited its potential use as a chemotherapeutic drug. This study aimed to examine the effectiveness of polyethylene glycol-polycaprolactone (PEG-PCL) as a nanocarrier for DAS to increase its anticancer capabilities. The DAS-loaded PEG-PCL nanoparticles (termed as DAS@PEG-PCL NPs) were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering (DLS). Morphological staining and MTT tests were employed to investigate drug-loaded nanoparticles' apoptotic and anti-proliferative effects. The MTT assay demonstrated that incorporating DAS onto PEG-PCL NPs resulted in a dose-dependent increase in cytotoxicity in A549 (lung cancer) and HeLa (cervical cancer) cells. The A549 cancer cells were analyzed for their morphology using the acridine orange/ethidium bromide (AO/EB) and DAPI staining techniques. Overall, these findings demonstrate that the polymeric PEG-PCL nanoparticle systems hold great potential as a novel therapeutic strategy for cancer treatment. 2024 Wiley Periodicals LLC. -
Facile fabrication of dasatinib laden multifunctional polymeric micelles: Evaluation of anti-proliferative and apoptotic activities in human cancer cells
Dasatinib (DAS) has recently gained significant interest for its anticancer potential. Yet, the lipophilicity inherent in DAS limited its potential use as a chemotherapeutic drug. This study aimed to examine the effectiveness of polyethylene glycol-polycaprolactone (PEG-PCL) as a nanocarrier for DAS to increase its anticancer capabilities. The DAS-loaded PEG-PCL nanoparticles (termed as DAS@PEG-PCL NPs) were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering (DLS). Morphological staining and MTT tests were employed to investigate drug-loaded nanoparticles' apoptotic and anti-proliferative effects. The MTT assay demonstrated that incorporating DAS onto PEG-PCL NPs resulted in a dose-dependent increase in cytotoxicity in A549 (lung cancer) and HeLa (cervical cancer) cells. The A549 cancer cells were analyzed for their morphology using the acridine orange/ethidium bromide (AO/EB) and DAPI staining techniques. Overall, these findings demonstrate that the polymeric PEG-PCL nanoparticle systems hold great potential as a novel therapeutic strategy for cancer treatment. 2024 Wiley Periodicals LLC. -
Facile fabrication of mesh-free, GO-reinforced ZrO2-based separators for advanced alkaline water electrolysis
Alkaline Water Electrolysis (AWE) is a promising method for sustainable hydrogen production due to its maturity and use of non-noble metal catalysts. A key challenge lies in developing cost-effective, durable, and scalable separators that ensure ionic conduction and separation between the electrodes. This study presents a mesh-free composite separator composed of zirconia nanoparticles (ZrO2 NPs), polysulfone (PSU), and graphene oxide (GO), eliminating the need for expensive polyphenylene sulphide (PPS) mesh and its hazardous hydrophilic surface treatments. GO was incorporated as a multifunctional additive to enhance mechanical strength, hydrophilicity, and dispersion of ZrO2 NPs. Separators were fabricated with varying compositions of ZrO2 NPs, PSU, and GO, and tested in a zero-gap titanium-based electrolyser using nickel foam electrodes and 30?wt% potassium hydroxide (KOH) electrolyte. Amongst them, the Sep72/25/3 separator (72?wt% ZrO2, 25?wt% PSU, 3?wt% GO) showed a low area-specific resistance (ASR) of 298?m? cm2 at room temperature (RT). It also exhibited excellent wettability with a reduced contact angle of 23 after 24?h conditioning in 30?wt% KOH, along with a notable improvement in tensile strength, from 1.75?MPa (without GO) to 3.26?MPa, validating the reinforcing role of GO. The results demonstrate a simple and scalable route for fabricating mesh-free separators that strike an optimal balance between ionic resistance, mechanical strength, and wettability, thereby offering a cost-effective alternative for next-generation advanced alkaline water electrolysis (AAWE) systems. The Korean Ceramic Society 2025. -
Facile fabrication of stable wettability gradients on elastomeric surfaces for applications in water collection and controlled cell adhesion
We have developed a simple and effective method to prepare stable wettability gradients on an elastomeric soft substrate, polydimethylsiloxane (PDMS). In our method, a partially cured PDMS film composed of a definite ratio of elastomer and crosslinking agent was heated over a hot surface with a temperature gradient. This causes differential thermal curing of the PDMS film and the water contact angle (wettability) of the resultant surface showed gradual variation across the length. This method allows us to design and fabricate wettability gradients with rationally controlled directionality and shapes (e.g., linear and radial gradients). The stability of the wettability gradients was studied and a chemical treatment method was developed to enhance the stability at room temperature. Stable wettability gradients prepared through this method can find applications as reliable platforms and scaffolds offering controlled or directional wetting and adhesion. We have demonstrated the practical applications of the wettability gradients in directional water collection, controlled crystallization of materials, and controlled cell adhesion of HeLa cells, osteoblasts and NIH/3T3 cells. The multi-functional characteristics of these wettable gradients are expected to be handy in other domains using soft materials and interfaces also. 2023 The Royal Society of Chemistry. -
Facile green synthesis of MnV2O6 nanoparticles: Photocatalytic studies and selective oxidation of aromatic alcohols
Advances in nanotechnology play a crucial role in developing reliable and environmentally friendly nanoparticles (NPs). The green synthesis method is one among them and aims to eliminate toxic by-products. Developing low-cost and highly efficient photocatalysts is essential to accelerate these reactions. To perform this, it is successfully synthesized manganese vanadate NPs using eco-friendly Butea monosperma leaves by the solution combustion method, and the synthesised NPs were characterized to examine their structural, optical, and morphological properties. The XRD pattern confirms that the synthesised MnV2O6 (MVO) NPs possess a monoclinic structure with an average crystallite size of about 67 nm. UVVis spectroscopy shows a band gap of 1.69 eV indicating the suitability of the materials in the Visible region. The photocatalytic activity of the resulting MVO NPs was evaluated and good photocatalytic activity for the degradation of methylene dye. Further, experiments were conducted at various parameters to optimize the catalyst and show the rate constant of 0.00467 min?1. catalytic activity of MVO NPs was also studied for the selective oxidation of aromatic alcohols. Among the various oxidizing agents and solvents used in optimization studies, tBuOOH (oxidizing agent) and CH3CN (solvent) showed the highest conversion (%) of benzyl alcohol, i.e., 98%. 2025 Elsevier B.V.