Matlab local directional number pattern for face analysis face and expression recognition
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Ecway Technologies is a company that provides IEEE projects and software developments with offices in multiple cities across India. They specialize in local directional number pattern analysis for face and expression recognition. This paper proposes a novel local feature descriptor called local directional number pattern (LDN) that encodes directional information of textures in a compact way, producing a more discriminative code than current methods. LDN extracts directional information from micro-patterns using a compass mask and encodes this using prominent direction indices and signs. The face is divided into regions and LDN features are extracted and concatenated into a feature vector to describe the face. Experiments show the descriptor performs well under variations in illumination, noise, expression, and time lapse. The
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IRJET-Face Recognition using LDN Code
J.K.Jeevitha ,B.Karthika,E.Devipriya "Face Recognition using LDN Code", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
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LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods. An LDN code is obtained by computing the edge response values in 8 directions at each pixel with the aid of a compass mask. Image analysis and understanding has recently received significant attention, especially during the past several years. At least two reasons can be accounted for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after nearly 30 years of research. In this paper we propose a novel local feature descriptor, called Local Directional Number Pattern (LDN), for face analysis, i.e., face and expression recognition. LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods.
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Abstract explanation should be Times New Roman, Font Size 10, Single line spacing, Italic, Text alignment should be justify, should
contain at least 250 words. Abstract explanation should be Times New Roman, Font Size 10, Single line spacing, Italic, Text
alignment should be justify, should contain at least 250 words. Abstract explanation should be Times New Roman, Font Size 10,
Single line spacing, Italic, Text alignment should be justify, should contain at least 250 words. Abstract explanation should be Times
New Roman, Font Size 10, Single line spacing, Italic, Text alignment should be justify, should contain at least 250 words. Abstract
explanation should be Times New Roman, Font Size 10, Single line spacing, Italic, Text alignment should be justify, should contain at
least 250 words. Abstract explanation should be Times New Roman, Font Size 10, Single line spacing, Italic, Text alignment should be
justify, should contain at least 250 words.
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This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY TECHNOLOGIES, an organization that offers software development and IEEE projects in various cities across India. The projects cover topics like stereo vision, change detection, image search, watermarking, face recognition, and vehicle detection using techniques such as reversible data hiding, wavelet transforms, and sparse representations.
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Local directional number pattern for face analysis face and expression recogn...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.orgFacial emotion recognition using deep learning detector and classifier
Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyperparameters, achieving an overall accuracy of 86.42% on the testing video dataset.
2014 ieee project matlab titles
This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY TECHNOLOGIES, an organization that offers software development and IEEE projects in various cities across India. The projects cover topics like stereo vision, change detection, image search, watermarking, face recognition, and vehicle detection using techniques such as reversible data hiding, wavelet transforms, and sparse representations.
2013 ieee project matlab titles
This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY Technologies, an organization that offers these projects and software developments in various cities across India. The projects cover topics such as stereo image processing, change detection in remote sensing images, image search, watermarking, face recognition, and vehicle detection in aerial images.
2013 ieee project matlab titles
This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY TECHNOLOGIES, an organization that offers software development and IEEE projects in various cities across India. The projects cover topics like stereo vision, change detection, image search, watermarking, face recognition, and vehicle detection using techniques such as reversible data hiding, wavelet transforms, and sparse representations.
2013 ieee project matlab titles
This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY TECHNOLOGIES, an organization that offers software development and IEEE projects in various cities across India. The projects cover topics like stereo vision, change detection, image search, watermarking, face recognition, and vehicle detection using techniques such as reversible data hiding, wavelet transforms, and sparse representations.
2014 ieee project matlab titles
This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY TECHNOLOGIES, an organization that offers software development and IEEE projects in various cities across India. The projects cover topics like stereo vision, change detection, image search, watermarking, face recognition, and vehicle detection using techniques such as reversible data hiding, wavelet transforms, and sparse representations.
2014 ieee project matlab titles
This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY TECHNOLOGIES, an organization that offers software development and IEEE projects in various cities across India. The projects cover topics like stereo vision, change detection, image search, watermarking, face recognition, and vehicle detection using techniques such as reversible data hiding, wavelet transforms, and sparse representations.
2014 ieee project matlab titles
This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY TECHNOLOGIES, an organization that offers software development and IEEE projects in various cities across India. The projects cover topics like stereo vision, change detection, image search, watermarking, face recognition, and vehicle detection using techniques such as reversible data hiding, wavelet transforms, and sparse representations.
2013 ieee project matlab titles
This document lists 20 IEEE projects from 2013 related to image processing, computer vision, and remote sensing. It provides contact information for ECWAY Technologies, an organization that offers these projects and software developments in various cities across India. The projects cover topics such as stereo image processing, change detection in remote sensing images, image search, watermarking, face recognition, and vehicle detection in aerial images.
Matlab linear distance coding for image classification
Ecway Technologies is an engineering company with offices in multiple cities in India. They provide IEEE projects and software development services. Their contact information is listed.
The document then describes a research paper that proposes a new image classification method called linear distance coding (LDC). LDC transforms local image features into distance vectors based on each image's distance to different classes. These distance vectors are encoded into sparse codes to capture salient image features. Experimental results on six image datasets show LDC outperforms traditional encoding methods and achieves state-of-the-art performance.
Generative Network for 3D Face Modelling
This document proposes a model for generating 3D face models from 2D images using deep learning techniques. Specifically, it uses a combination of convolutional neural networks (CNNs) and generative adversarial networks (GANs). CNNs are used to extract feature maps from 2D images, and GANs generate the 3D face model from those feature maps. The model is trained on a dataset of 30 individuals and is able to generate 3D face models with high accuracy, even when trained on limited data. Evaluation shows the model performs well and improves the inception score for 3D face generation compared to other techniques.
Local directional number pattern for face analysis face and expression recogn...
Local directional number pattern for face analysis face and expression recogn...JPINFOTECH JAYAPRAKASH
This paper proposes a new local feature descriptor called local directional number pattern (LDN) to encode structural information from textures for face recognition and expression analysis. LDN analyzes directional information in local neighborhoods using a compass mask to determine prominent positive and negative directions, allowing it to distinguish textures with similar patterns but different intensity transitions. It uses gradient information across the entire neighborhood, conveying more information compactly compared to other descriptors. Experiments show LDN performs consistently under variations in illumination, noise, expression, and time.3D Face Modelling of Human Faces using GANs
This document describes a model for 3D face modelling using GANs with less training data. Convolutional neural networks (CNNs) are used to extract features from 2D face images. These features are then used to generate 3D face models using generative adversarial networks (GANs). The model measures distances between facial features and calculates regional areas of faces from different views to capture spatial data needed for 3D modelling. Evaluation on datasets shows the model can generate realistic 3D face models with high accuracy, even with limited training data.
Volume 2-issue-6-2108-2113
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
Volume 2-issue-6-2108-2113
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a
new scheme to extract “Multi-Directional Multi-Level Dual-Cross Patterns” (MDML-DCPs) from face images. Specifically, the MDMLDCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations.
Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g. LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.
A face recognition system using convolutional feature extraction with linear...
Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).
Matlab interactive segmentation for change detection in multispectral remote...
This document summarizes a proposed method for interactive change detection in multispectral remote sensing images. The method uses user-provided markers for change and no-change classes to train a support vector machine classifier, generating a spectral change map. Two approaches are described to incorporate spatial context - a Markov random field method that is region-driven, and a level-set method that exploits both regions and contours. Experiments on real remote sensing images showing different types of changes demonstrate that the interactive methods can accurately generate change detection maps with simple minimal user interaction.
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Facial emotion recognition using deep learning detector and classifier
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Local directional number pattern for face analysis face and expression recogn...
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Matlab local directional number pattern for face analysis face and expression recognition
- 1. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE BANGALORE / HYDRABAD CELL: +91 98949 17187, +91 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 / 8111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com LOCAL DIRECTIONAL NUMBER PATTERN FOR FACE ANALYSIS: FACE AND EXPRESSION RECOGNITION ABSTRACT: This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts directional information, and we encode such information using the prominent direction indices (directional numbers) and sign-which allows us to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, and extract the distribution of the LDN features from them. Then, we concatenate these features into a feature vector, and we use it as a face descriptor. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression, and time lapse variations. Moreover, we test our descriptor with different masks to analyze its performance in different face analysis tasks.