IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Facial recognition using modified local binary pattern and random forestijaia
This paper presents an efficient algorithm for face recognition using the local binary pattern (LBP) and
random forest (RF). The novelty of this research effort is that a modified local binary pattern (MLBP),
which combines both the sign and magnitude features for the improvement of facial texture classification
performance, is applied. Furthermore, RF is used to select the most important features from the extracted
feature sequence. The performance of the proposed scheme is validated using a complex dataset, namely
Craniofacial Longitudinal Morphological Face (MORPH) Album 1 dataset
Automatic face naming by learning discriminativenexgentech15
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
AUTOMATIC FACE NAMING BY LEARNING DISCRIMINATIVE AFFINITY MATRICES FROM WEAKL...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
AUTOMATIC FACE NAMING BY LEARNING DISCRIMINATIVE AFFINITY MATRICES FROM WEAKL...nexgentechnology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Automatic face naming by learning discriminative affinity matrices from weakl...Shakas Technologies
Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face
Statistical Models for Face Recognition System With Different Distance MeasuresCSCJournals
Face recognition is one of the challenging applications of image processing. Robust face recognition algorithm should posses the ability to recognize identity despite many variations in pose, lighting and appearance. Principle Component Analysis (PCA) method has a wide application in the field of image processing for dimension reduction of the data. But these algorithms have certain limitations like poor discriminatory power and ability to handle large computational load. This paper proposes a face recognition techniques based on PCA with Gabor wavelets in the preprocessing stage and statistical modeling methods like LDA and ICA for feature extraction. The classification for the proposed system is done using various distance measure methods like Euclidean Distance(ED), Cosine Distance (CD), Mahalanobis Distance (MHD) methods and the recognition rate were compared for different distance measures. The proposed method has been successfully tested on ORL face data base with 400 frontal images corresponding to 40 different subjects which are acquired under variable illumination and facial expressions. It is observed from the results that use of PCA with Gabor filters and features extracted through ICA method gives a recognition rate of about 98% when classified using Mahalanobis distance classifier. This recognition rate stands better than the conventional PCA and PCA + LDA methods employing other and classifier techniques.
Pose and Illumination in Face Recognition Using Enhanced Gabor LBP & PCA IJMER
This paper presents the face recognition based on Enhanced GABOR LBP and PCA. Some
of the challenges in face recognition are occlusion, pose and illumination .In this paper, we are more
focused on varying pose and illumination. We divided this algorithm into five stages. First stage finds
the fiducial points on face using Gabor filter bank as this filter is well known for illumination
compensation. Second stage applies the morphological techniques for reduce useless fiducial points.
Third stage applies the LBP on reduced fiducial points with neighborhood pixel for improving the pose
variation. Forth stage uses PCA to detect the best variance points which are necessary to characterize
the training images. The last recognition stage includes finding the Euclidean norm of the feature
weight vectors with the test weight vector. In this project, we used 20 images of 20 different persons
from ORL database for training. For testing, we used images with varying illumination, pose and
occluded images of the same training
Utilizing image scales towards totally training free blind image quality asse...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
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IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
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Facial recognition using modified local binary pattern and random forestijaia
This paper presents an efficient algorithm for face recognition using the local binary pattern (LBP) and
random forest (RF). The novelty of this research effort is that a modified local binary pattern (MLBP),
which combines both the sign and magnitude features for the improvement of facial texture classification
performance, is applied. Furthermore, RF is used to select the most important features from the extracted
feature sequence. The performance of the proposed scheme is validated using a complex dataset, namely
Craniofacial Longitudinal Morphological Face (MORPH) Album 1 dataset
Automatic face naming by learning discriminativenexgentech15
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
AUTOMATIC FACE NAMING BY LEARNING DISCRIMINATIVE AFFINITY MATRICES FROM WEAKL...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
AUTOMATIC FACE NAMING BY LEARNING DISCRIMINATIVE AFFINITY MATRICES FROM WEAKL...nexgentechnology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Automatic face naming by learning discriminative affinity matrices from weakl...Shakas Technologies
Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face
Statistical Models for Face Recognition System With Different Distance MeasuresCSCJournals
Face recognition is one of the challenging applications of image processing. Robust face recognition algorithm should posses the ability to recognize identity despite many variations in pose, lighting and appearance. Principle Component Analysis (PCA) method has a wide application in the field of image processing for dimension reduction of the data. But these algorithms have certain limitations like poor discriminatory power and ability to handle large computational load. This paper proposes a face recognition techniques based on PCA with Gabor wavelets in the preprocessing stage and statistical modeling methods like LDA and ICA for feature extraction. The classification for the proposed system is done using various distance measure methods like Euclidean Distance(ED), Cosine Distance (CD), Mahalanobis Distance (MHD) methods and the recognition rate were compared for different distance measures. The proposed method has been successfully tested on ORL face data base with 400 frontal images corresponding to 40 different subjects which are acquired under variable illumination and facial expressions. It is observed from the results that use of PCA with Gabor filters and features extracted through ICA method gives a recognition rate of about 98% when classified using Mahalanobis distance classifier. This recognition rate stands better than the conventional PCA and PCA + LDA methods employing other and classifier techniques.
Pose and Illumination in Face Recognition Using Enhanced Gabor LBP & PCA IJMER
This paper presents the face recognition based on Enhanced GABOR LBP and PCA. Some
of the challenges in face recognition are occlusion, pose and illumination .In this paper, we are more
focused on varying pose and illumination. We divided this algorithm into five stages. First stage finds
the fiducial points on face using Gabor filter bank as this filter is well known for illumination
compensation. Second stage applies the morphological techniques for reduce useless fiducial points.
Third stage applies the LBP on reduced fiducial points with neighborhood pixel for improving the pose
variation. Forth stage uses PCA to detect the best variance points which are necessary to characterize
the training images. The last recognition stage includes finding the Euclidean norm of the feature
weight vectors with the test weight vector. In this project, we used 20 images of 20 different persons
from ORL database for training. For testing, we used images with varying illumination, pose and
occluded images of the same training
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Face recognition by exploiting local gabor
1. Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Title: Face Recognition by Exploiting Local Gabor
Features With Multitask Adaptive Sparse
Representation
Abstract—For a human face, the Gabor transform can extract its multiple scale and
orientation features that are very useful for the recognition. In this paper, the
Gabor-feature-based facerecognition is formulated as a multitask sparse
representation model, in which the sparse coding of each Gabor feature is regarded
as a task. To effectively exploit the complementary yet correlated information
among different tasks, a flexiblerepresentation algorithm termed multitask adaptive
sparse representation (MASR) is proposed. The MASR algorithm not only restricts
Gabor features of one test sample to be jointlyrepresented by training atoms from
the same class but also promotes the selected atoms for these features to be varied
within each class, thus allowing better representation. In addition, to use the local
information, we operate the MASR on local regions of Gabor features. Then, by
considering the structural characteristics of the face and the effects of the external
interferences, a structural–residual weighting strategy is proposed to adaptively
fuse the decision of each region. Experiments on various datasets verify the
effectiveness of the proposed method in dealing with face occlusion, corruption,
small number of training samples, as well as variations of lighting and expression.
2. Software & Hardware requirement:
1. Hardware requirement:
1. PC
2. RAM minimum 2GB
3. HDD minimum 100GB
2. Software requirement:
1. MATLAB 7.0
2. Signal processing toolbox
3. Image processing toolbox
4. Mathematical toolbox
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper
Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement
(Modification)
For More Details please contact
Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com