scholarly journals Elastic Bunch Graph Matching Based Face Recognition Under Varying Lighting, Pose, and Expression Conditions

2016 ◽  
Vol 3 (4) ◽  
pp. 177 ◽  
Author(s):  
Farooq Ahmad Bhat ◽  
M. Arif Wani
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Graph Matching ◽  
Experimental Results ◽  
Lighting Condition ◽  
Expression Variation ◽  
Labelled Graphs

In this paper performance of elastic bunch graph matching (EBGM) for face recognition under variation in facial expression, variation in lighting condition and variation in poses are given. In this approach faces are represented by labelled graphs. Experimental results of EBGM on ORL, Yale B and FERET datasets are provided. Strong and weak features of EBGM algorithm are discussed.

AN EVALUATION ON PERFORMANCE OF PCA IN FACE RECOGNITION WITH EXPRESSION VARIATIONS

2018 ◽  
Vol 1 (30) ◽  
pp. 61-66
Author(s):  
Khanh Ngoc Van Duong ◽  
An Bao Nguyen
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Mouth Opening ◽  
Experimental Results ◽  
Classification Algorithms ◽  
Test Cases ◽  
Wide Mouth ◽  
Better Than

Appearance-based recognition methods often encounter difficulties when the input images contain facial expression variations such as laughing, crying or wide mouth opening. In these cases, holistic methods give better performance than appearance-based methods. This paper presents some evaluation on face recognition under variation of facial expression  using the combination of PCA and classification algorithms. The experimental results showed that the best accuracy can be obtained with very few eigenvectors and KNN algorithm (with k=1) performs better than SVM in most test cases.

scholarly journals Emotion Monitoring for Preschool Children Based on Face Recognition and Emotion Recognition Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guiping Yu
Keyword(s):  
Face Recognition ◽  
Preschool Children ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Experimental Results ◽  
Geometric Feature ◽  
Feature Descriptor ◽  
Expression Recognition ◽  
Facial Contour ◽  
Recognition Algorithms

In this paper, we study the face recognition and emotion recognition algorithms to monitor the emotions of preschool children. For previous emotion recognition focusing on faces, we propose to obtain more comprehensive information from faces, gestures, and contexts. Using the deep learning approach, we design a more lightweight network structure to reduce the number of parameters and save computational resources. There are not only innovations in applications, but also algorithmic enhancements. And face annotation is performed on the dataset, while a hierarchical sampling method is designed to alleviate the data imbalance phenomenon that exists in the dataset. A new feature descriptor, called “oriented gradient histogram from three orthogonal planes,” is proposed to characterize facial appearance variations. A new efficient geometric feature is also proposed to capture facial contour variations, and the role of audio methods in emotion recognition is explored. Multifeature fusion can be used to optimally combine different features. The experimental results show that the method is very effective compared to other recent methods in dealing with facial expression recognition problems about videos in both laboratory-controlled environments and outdoor environments. The method performed experiments on expression detection in a facial expression database. The experimental results are compared with data from previous studies and demonstrate the effectiveness of the proposed new method.

Fusion of face recognition and facial expression detection for authentication

2017 ◽  
Author(s):  
Delina Beh Mei Yin ◽  
Shariman Omar ◽  
Bazilah A. Talip ◽  
Amalia Muklas ◽  
Nur Afiqah Mohd Norain ◽  
...  
Keyword(s):  
Face Recognition ◽  
Facial Expression

Face Recognition Algorithm Based on Kernel Collaborative Representation

2013 ◽  
Vol 756-759 ◽  
pp. 3590-3595
Author(s):  
Liang Zhang ◽  
Ji Wen Dong
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Collaborative Representation ◽  
Nonlinear Structures ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
The Relationship

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

scholarly journals Illumination Invariant Face Recognition

2020 ◽  
Vol 10 (3) ◽  
pp. 129
Author(s):  
Regina Lionnie ◽  
Mochamad Miftakhul Huda ◽  
Mudrik Alaydrus
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Cross Validation ◽  
Graph Matching ◽  
Illumination Invariant ◽  
Fold Cross Validation

Face recognition adalah bidang penelitian yang selalu menjadi topik penelitian dengan peminatan yang sangat besar. Berbagai potensial pengembangan aplikasi, dari sistem keamanan individu hingga untuk sistem control dan sistem surveillance. Algoritma pengenalan wajah telah diusulkan oleh banyak peneliti. Metode pengenalan wajah dengan performa yang baik seperti eigenfaces, fisherfaces, jaringan saraf tiruan, elastic bunch graph matching, laplacian faces, dan lainnya. Performa dari algoritma ini awalnya diuji pada gambar wajah yang dikumpulkan di bawah lingkungan kontrol yang baik pada kondisi studio dan pencahayaan yang diatur, dan karenanya, sebagian besar mengalami kesulitan dalam mengatasi gambar alami, yang dapat ditangkap di bawah kondisi pencahayaan, pose, dan ekspresi wajah yang sangat bervariasi. Situasi menjadi lebih menantang ketika kombinasi variasi ini harus ditangani secara bersamaan. Kondisi pencahayaan berbeda menimbulkan hambatan vital dalam sistem pengenalan karena mereka sangat mempengaruhi penampilan gambar wajah dan meningkatkan variasi antar kelas. Pada penelitian ini, telah dibangun sistem pengenalan wajah menggunakan Local Binary Pattern (LBP) dengan total gambar pada basis data sebanyak 400 gambar yang diambil dari 25 kelas/responden. Menggunakan 2-fold cross validation dan jarak Euclidean, presisi tertinggi yang diraih system adalah sebesar 87,98% dengan variasi ekualisasi histogram tanpa menggunakan LBP.

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

scholarly journals The Effect of Pre-Adaptation Lighting Condition on the Experimental Results of Emotional Lighting

2015 ◽  
Vol 29 (1) ◽  
pp. 141-148
Author(s):  
곽영신 ◽  
오세민 ◽  
최진숙 ◽  
김성필 ◽  
김치중
Keyword(s):  
Experimental Results ◽  
Lighting Condition

Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

2019 ◽  
pp. 258-279
Author(s):  
Lavika Goel ◽  
Lavanya B. ◽  
Pallavi Panchal
Keyword(s):  
Face Recognition ◽  
Evolutionary Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Recognition Rate ◽  
Experimental Results ◽  
Nature Inspired Algorithm ◽  
In The Wild ◽  
Gravitational Search

This chapter aims to apply a novel hybridized evolutionary algorithm to the application of face recognition. Biogeography-based optimization (BBO) has some element of randomness to it that apart from improving the feasibility of a solution could reduce it as well. In order to overcome this drawback, this chapter proposes a hybridization of BBO with gravitational search algorithm (GSA), another nature-inspired algorithm, by incorporating certain knowledge into BBO instead of the randomness. The migration procedure of BBO that migrates SIVs between solutions is done between solutions only if the migration would lead to the betterment of a solution. BBO-GSA algorithm is applied to face recognition with the LFW (labelled faces in the wild) and ORL datasets in order to test its efficiency. Experimental results show that the proposed BBO-GSA algorithm outperforms or is on par with some of the nature-inspired techniques that have been applied to face recognition so far by achieving a recognition rate of 80% with the LFW dataset and 99.75% with the ORL dataset.

Sign in / Sign up

Export Citation Format

Share Document