Bücher von Anilkumar Suthar

In der schnell wachsenden Welt ist die Gesichtserkennung eine große Herausforderung, da es eine Vielzahl von Gesichtern im Universum gibt und die Komplexität von Geräuschen und Hintergründen die Gesichtserkennung zu einem der wichtigsten Forschungsgebiete macht. Sie hat eine Reihe von Anwendungen und Einsatzmöglichkeiten. Es werden viele Methoden und Algorithmen vorgeschlagen. Die Gesichtserkennung fällt unter die biometrische Identifizierung wie Iris, Retina, Fingerabdrücke usw. Die Merkmale des Gesichts werden als biometrische Identifikatoren bezeichnet. Die biometrischen Identifikatoren sind nicht leicht zu fälschen, zu verlegen oder weiterzugeben, so dass der Zugang über biometrische Identifikatoren eine bessere und sicherere Möglichkeit bietet, Dienstleistungen und Sicherheit zu bieten. Wir können auch viele intelligente Anwendungen entwickeln, die Sicherheit und Identität bieten können. Wir schlagen eine Arbeit zur Gesichtserkennung vor, bei der bestimmte Algorithmen, nämlich ein zweistufiges neuronales Faltungsnetzwerk (CNN) und eine Support-Vektor-Maschine, für die Klassifizierung von Merkmalen verwendet werden. Das neuronale Faltungsnetzwerk (CNN) dient der Merkmalsextraktion, und mit Hilfe dieses Algorithmus wird versucht, eine genaue und effektive Gesichtserkennung durchzuführen.

En un mundo cada vez más rápido, el reconocimiento facial es todo un reto, ya que hay una gran variedad de rostros en el universo y la complejidad de los ruidos y los fondos hace que el reconocimiento facial sea una de las principales áreas de investigación. Tiene numerosas aplicaciones y usos. Se han propuesto muchos métodos y algoritmos. El reconocimiento facial forma parte de la identificación biométrica, como el iris, la retina, las huellas dactilares, etc. Las características de la cara se denominan identificadores biométricos. Los identificadores biométricos no son fáciles de falsificar, extraviar o compartir, por lo que el acceso a través de un identificador biométrico nos ofrece una forma más segura de proporcionar servicios y seguridad. También podemos desarrollar muchas aplicaciones inteligentes que pueden proporcionar seguridad e identidad. Proponemos un trabajo sobre la detección facial en el que ciertos algoritmos que son dos etapas Convolution Neural Network (CNN) y Support-Vector Machine se utilizan básicamente para la clasificación de características y la Convolution Neural Network (CNN) es la extracción de características y mediante el uso de este algoritmo tratará de proporcionar una detección precisa y eficaz de la cara.

No mundo em crescimento rápido, o reconhecimento facial é um desafio silencioso, pois existem variedades de rostos no universo e a complexidade dos ruídos e dos antecedentes O Reconhecimento Facial é uma das áreas chave sob investigação. Tem inúmeras aplicações e utilizações. Muitos métodos e algoritmos são apresentados. O Reconhecimento Facial está sob identificação Bio métrica como íris, retina, impressões digitais, etc. As características do rosto são chamadas identificadores biométricos. Os identificadores bio-métricos não são facilmente forjados; o acesso através do identificador bio-métrico não é facilmente forjado ou partilhado, pelo que o acesso através do identificador bio-métrico nos dá uma forma mais segura de fornecer serviço e segurança. Podemos também desenvolver muitas aplicações inteligentes que podem fornecer segurança e identidade. Propomos um trabalho de Detecção facial em que certos algoritmos que são duas fases Rede Neural de Convolução (CNN) e Máquina de Apoio Vectorial são basicamente utilizados para classificação de características e a Rede Neural de Convolução (CNN) é a extracção de características e ao utilizar este algoritmo tentará fornecer uma Detecção Facial precisa e eficaz.

V bystro razwiwaüschemsq mire raspoznawanie lic qwlqetsq slozhnoj zadachej, poskol'ku wo wselennoj suschestwuet mnozhestwo lic, a takzhe slozhnost' shumow i fona Raspoznawanie lic qwlqetsq odnoj iz klüchewyh oblastej issledowanij. Ono imeet mnozhestwo primenenij i sposobow ispol'zowaniq. Predlozheno mnozhestwo metodow i algoritmow. Raspoznawanie lic otnositsq k biometricheskoj identifikacii, kak raduzhnaq obolochka glaza, setchatka glaza, otpechatki pal'cew i t.d. Osobennosti lica nazywaütsq biometricheskimi identifikatorami. Biometricheskie identifikatory nelegko poddelat', pereputat' ili razdelit', poätomu dostup s pomosch'ü biometricheskogo identifikatora daet nam bolee nadezhnyj sposob predostawleniq uslug i obespecheniq bezopasnosti. My takzhe mozhem razrabotat' mnozhestwo intellektual'nyh prilozhenij, kotorye mogut obespechit' bezopasnost' i identifikaciü. My predlagaem rabotu po obnaruzheniü lic, w kotoroj opredelennye algoritmy, kotorye qwlqütsq dwumq ätapami Konwolücionnaq nejronnaq set' (CNN) i Support-Vector Machine, w osnownom ispol'zuütsq dlq klassifikacii priznakow, a Konwolücionnaq nejronnaq set' (CNN) - dlq izwlecheniq priznakow, i s pomosch'ü ätogo algoritma my popytaemsq obespechit' tochnoe i äffektiwnoe obnaruzhenie lic.

In un mondo in rapida crescita, il riconoscimento facciale è una sfida tranquilla, data la varietà di volti presenti nell'universo e la complessità dei rumori e degli sfondi Il riconoscimento facciale è una delle aree chiave della ricerca. Ha numerose applicazioni e utilizzi. Sono stati proposti molti metodi e algoritmi. Il riconoscimento dei volti rientra nell'ambito dell'identificazione bio metrica, come l'iride, la retina, le impronte digitali, ecc. Le caratteristiche del volto sono chiamate identificatori bio metrici. Gli identificatori biometrici non sono facilmente falsificabili, smarribili o condivisibili, quindi l'accesso attraverso l'identificatore biometrico ci offre un modo più sicuro per fornire servizi e sicurezza. Possiamo anche sviluppare molte applicazioni intelligenti che possono fornire sicurezza e identità. Proponiamo un lavoro sul rilevamento del volto in cui alcuni algoritmi a due stadi, la Rete neurale a convoluzione (CNN) e la Support-Vector Machine, sono utilizzati per la classificazione delle caratteristiche e la Rete neurale a convoluzione (CNN) per l'estrazione delle caratteristiche e, utilizzando questo algoritmo, cercheremo di fornire un rilevamento del volto accurato ed efficace.

Dans un monde en pleine expansion, la reconnaissance faciale est un véritable défi, car il existe une grande variété de visages dans l'univers et la complexité des bruits et des arrière-plans La reconnaissance faciale est l'un des principaux domaines de recherche. Elle a un certain nombre d'applications et d'utilisations. De nombreuses méthodes et algorithmes sont proposés. La reconnaissance des visages fait partie de l'identification bio métrique comme l'iris, la rétine, les empreintes digitales, etc. Les caractéristiques du visage sont appelées identifiants bio métriques. Les caractéristiques du visage sont appelées identificateurs bio métriques. Les identificateurs bio métriques ne sont pas faciles à falsifier, à égarer ou à partager ; l'accès par l'intermédiaire d'un identificateur bio métrique constitue donc un moyen plus sûr de fournir des services et d'assurer la sécurité. Nous pouvons également développer de nombreuses applications intelligentes qui peuvent assurer la sécurité et l'identité. Nous proposons un travail sur la détection des visages dans lequel certains algorithmes, à savoir le réseau neuronal à convolution (CNN) et la machine à vecteur de support, sont utilisés pour la classification des caractéristiques et le réseau neuronal à convolution (CNN) pour l'extraction des caractéristiques.

Biomedical Image Processing consists many different types of imaging methods likes CT scans, X-Ray and MRI. These techniques allow humans to identify even the smallest abnormalities in the human body. The primary goal of medical imaging is to extract meaningful and accurate information from the images with the least error possible. MRI (Magnetic Resonance Imaging) is a medical technique, mainly used by the radiologist for visualization of internal structure of the human body. MRI provides useful information about the human soft tissue, which helps in the diagnosis of brain tumor. Image segmentation refers to partitioning of image into multiple regions or segments such that it can meaningfully represent the image through which information can be extracted. In this paper we are using Canny and SIFT techniques for segmentation of brain image considering shape and texture features. After that Support Vector Machine (SVM) is used to classify tumor and non-tumor regions. The performance of the proposed method is evaluated in terms of Sensitivity (Se), specificity (Sp), precision (Pr) and accuracy (Acc) and PSNR.

Speaker Recognition is used for identification of a person depending on the characteristics contained in the speech signal. In this paper we propose the use of Deep Neural Network (DNN) for text dependent speaker Recognition system (SRS). Mel Frequency Cepstral Coefficients (MFCC) and Auto-encoder (Butterfly Structure Neural Network) are used to extract the features of speech signal at the initial stage. The previously obtained coefficients are then used to train the DNN to later classify the speakers. DNN can be directly used to extract features and classify speakers but the MFCC and Auto-encoder are used initially for data compression and maximum number of feature extraction thus aiming to get better efficiency and faster results.

Global Positioning System (GPS) could be a navigation system using satellites that shows location and time data all told atmospheric condition to the user, orthogonal of position. GPS technology uses satellites and ground stations that has created a bearing on navigation and positioning desires of people. It's the flexibility to trace craft, cars, cell phones, boats and even people that become a reality. These all confer with out of doors navigation that¿s simply potential with the assistance of GPS navigation. However what if we wish to navigate in indoor environments? To achieve correct Indoor Position, we tend to could use the thought of increased Reality. It has been associate degree upward trend between the demand for indoor positioning systems victimization Wi-Fi, Bluetooth low energy, and visual light-weight communication. Numerous centroid Localization Algorithms are used. During this paper, we've got discuss one among the centroid localization algorithms i.e. Weighted centroid Localization (WCL) rule victimization Received Signal Strength Indicator (RSSI) measured by nearby anchors nodes. The WCL is simulated in an exceedingly explicit setting and analyzed to tac

In growing fast world facial recognition is quiet challenging as there are varieties of faces in the universe and the complexity of noises and backgrounds Face Recognition is one of the key areas under research. It has number of applications and uses. Many methods and algorithms are put forward. Face recognition comes under Bio metric identification like iris, retina, finger prints etc. The features of the face are called bio metric identifiers. The bio metric identifiers are not easily forged; misplaced or shared hence access through bio metric identifier gives us a better secure way to provide service and security. We can also develop many intelligent applications which may provide security and identity. We propose a work on facial Detection in which certain algorithms that are two stages Convolution Neural Network (CNN) and Support-Vector Machine are basically used for feature classification and the Convolution Neural Network (CNN) is feature extraction and by using this algorithm will try to provide accurate and effective Face Detection.