scholarly journals Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

2021 ◽  
Vol 15 (3) ◽  
pp. 47-57
Author(s):  
Amit Kumar Yadav ◽  
Neeraj Gupta ◽  
Aamir Khan ◽  
Anand Singh Jalal
Keyword(s):  
Face Recognition ◽  
Kernel Method ◽  
Partial Occlusion ◽  
Scale Invariant ◽  
Query Image ◽  
Art Methods ◽  
Constrained Environments ◽  
The Face ◽  
Robust Face Recognition ◽  
Scale Invariant Feature

Face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics, and so on. It is a challenging task in the field of computer vision. Although the various state-of-the-art methods of face recognition in constrained environments have achieved satisfactory results, there are still many issues which are untouched in unconstrained environments, such as partial occlusions, large pose variations, etc. In this paper, the authors have proposed an approach which utilized the local generic feature (LGF) to recognize the face in the partial occlusion by fusing features scale invariant feature transform (SIFT) and multi-block local binary pattern (MB-LBP). It also utilizes robust kernel method for classification of the query image. They have validated the effectiveness of the proposed approach on the benchmark AR face database. The experimental outcomes illustrate that the proposed approach outperformed the state-of-art methods for robust face recognition.

scholarly journals DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION

2017 ◽  
Vol 1 (28) ◽  
pp. 56-63
Author(s):  
Khanh Ngan Chau ◽  
Nghi Thanh Doan
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Human Face ◽  
Scale Invariant ◽  
Adaboost Algorithm ◽  
The Face ◽  
Benchmark Datasets ◽  
Scale Invariant Feature

Human face recognition is a technology which is widely used in life. There have been much effort on developing face recognition algorithms. In this paper, we present a new methodology that combines Haar Like Features - Cascade of Boosted Classifiers, Dense Scale-Invariant Feature Transform (DSIFT), Local Naive Bayes Nearest Neighbor (LNBNN) algorithm for the recognition of human face. We use Haar Like Features and the combination  of AdaBoost algorithm and Cascade stratified model to detect and extract the face image, the DSIFT descriptors of the image are computed only for the aligned and cropped face image.Then, we apply the LNBNN algorithms for object recognition. Numerical testing on several benchmark datasets using our proposed method for facerecognition gives the better results than other methods. The accuracies obtained by LNBNN method is 99.74 %.

scholarly journals Template Based Pose and Illumination Invariant Face Recognition

2020 ◽  
Vol 8 (5) ◽  
pp. 3220-3229
Keyword(s):  
Face Recognition ◽  
Gabor Wavelet ◽  
Mathematical Methods ◽  
Query Image ◽  
Illumination Invariant ◽  
Face Images ◽  
The Face ◽  
Similar Images ◽  
Proper Face ◽  
The Impact

This article presents a method “Template based pose and illumination invariant face recognition”. We know that pose and Illumination are important variants where we cannot find proper face images for a given query image. As per the literature, previous methods are also not accurately calculating the pose and Illumination variants of a person face image. So we concentrated on pose and Illumination. Our System firstly calculates the face inclination or the pose of the head of a person with various mathematical methods. Then Our System removes the Illumination from the image using a Gabor phase based illumination invariant extraction strategy. In this strategy, the system normalizes changing light on face images, which can decrease the impact of fluctuating Illumination somewhat. Furthermore, a lot of 2D genuine Gabor wavelet with various orientations is utilized for image change, and numerous Gabor coefficients are consolidated into one entire in thinking about spectrum and phase. Finally, the light invariant is acquired by separating the phase feature from the consolidated coefficients. Then after that, the obtained Pose and illumination invariant images are convolved with Gabor filters to obtain Gabor images. Then templates will be extracted from these Gabor images and one template average is generated. Then similarity measure will be performed between query image template average and database images template averages. Finally the most similar images will be displayed to the user. Exploratory results on PubFig database, Yale B and CMU PIE face databases show that our technique got a critical improvement over other related strategies for face recognition under enormous pose and light variation conditions.

scholarly journals TOWARDS ADAPTIVE HIGH-RESOLUTION IMAGES RETRIEVAL SCHEMES

2016 ◽  
Vol III-2 ◽  
pp. 181-189
Author(s):  
A. Kourgli ◽  
H. Sebai ◽  
S. Bouteldja ◽  
Y. Oukil
Keyword(s):  
High Spatial Resolution ◽  
Scale Invariant ◽  
Query Image ◽  
Multi Scale ◽  
Adaptive Schemes ◽  
Retrieval Scheme ◽  
First Results ◽  
High Resolution Images ◽  
Scale Invariant Feature ◽  
Complex Methods

Nowadays, content-based image-retrieval techniques constitute powerful tools for archiving and mining of large remote sensing image databases. High spatial resolution images are complex and differ widely in their content, even in the same category. All images are more or less textured and structured. During the last decade, different approaches for the retrieval of this type of images have been proposed. They differ mainly in the type of features extracted. As these features are supposed to efficiently represent the query image, they should be adapted to all kind of images contained in the database. However, if the image to recognize is somewhat or very structured, a shape feature will be somewhat or very effective. While if the image is composed of a single texture, a parameter reflecting the texture of the image will reveal more efficient. This yields to use adaptive schemes. For this purpose, we propose to investigate this idea to adapt the retrieval scheme to image nature. This is achieved by making some preliminary analysis so that indexing stage becomes supervised. First results obtained show that by this way, simple methods can give equal performances to those obtained using complex methods such as the ones based on the creation of bag of visual word using SIFT (Scale Invariant Feature Transform) descriptors and those based on multi scale features extraction using wavelets and steerable pyramids.

scholarly journals Fourier Spectrum Features for Face Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 2632-2636
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Fourier Spectrum ◽  
Partial Occlusion ◽  
Novel Approach ◽  
The Face ◽  
Unsupervised Methods

The objective is to introduce a novel approach which deals with the challenges: uneven illumination and partial occlusion. This method performs face recognition by extracting the magnitude spectra features. At each point on the face, largest matching areas were found. Thus robustness is achieved using Fourier magnitude spectra feature extraction and largest matching area comparison. This method performs competitively with corrupted images and other unsupervised methods. The proposed approach is experimented on Yale B and AR datasets.

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

2008 ◽  
pp. 188-211
Author(s):  
Ting Shan ◽  
Abbas Bigdeli ◽  
Brian C. Lovell ◽  
Shaokang Chen
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
Principal Component ◽  
Recognition System ◽  
Active Appearance Models ◽  
Face Recognition System ◽  
The Face ◽  
The Embedded System ◽  
Computationally Intensive ◽  
Robust Face Recognition

In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

scholarly journals Robust Face Recognition Providing the Identity and Its Reliability Degree Combining Sparse Representation and Multiple Features

2016 ◽  
Vol 30 (10) ◽  
pp. 1656007 ◽  
Author(s):  
Giuliano Grossi ◽  
Raffaella Lanzarotti ◽  
Jianyi Lin
Keyword(s):  
Face Recognition ◽  
Sparse Coding ◽  
Research Effort ◽  
Multiple Features ◽  
Feature Representations ◽  
Human Ability ◽  
The Face ◽  
Human Skills ◽  
Robust Face Recognition ◽  
Multistage Classifier

For decades, face recognition (FR) has attracted a lot of attention, and several systems have been successfully developed to solve this problem. However, the issue deserves further research effort so as to reduce the still existing gap between the computer and human ability in solving it. Among the others, one of the human skills concerns his ability in naturally conferring a “degree of reliability” to the face identification he carried out. We believe that providing a FR system with this feature would be of great help in real application contexts, making more flexible and treatable the identification process. In this spirit, we propose a completely automatic FR system robust to possible adverse illuminations and facial expression variations that provides together with the identity the corresponding degree of reliability. The method promotes sparse coding of multi-feature representations with LDA projections for dimensionality reduction, and uses a multistage classifier. The method has been evaluated in the challenging condition of having few (3–5) images per subject in the gallery. Extended experiments on several challenging databases (frontal faces of Extended YaleB, BANCA, FRGC v2.0, and frontal faces of Multi-PIE) show that our method outperforms several state-of-the-art sparse coding FR systems, thus demonstrating its effectiveness and generalizability.

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