scholarly journals Research on Face Recognition Method by Autoassociative Memory Based on RNNs

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qi Han ◽  
Zhengyang Wu ◽  
Shiqin Deng ◽  
Ziqiang Qiao ◽  
Junjian Huang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Design Procedure ◽  
Biological Database ◽  
Model Parameters ◽  
Face Database ◽  
Recognition Method ◽  
The Face ◽  
Model Training ◽  
And Performance ◽  
The Stability

In order to avoid the risk of the biological database being attacked and tampered by hackers, an Autoassociative Memory (AAM) model is proposed in this paper. The model is based on the recurrent neural networks (RNNs) for face recognition, under the condition that the face database is replaced by its model parameters. The stability of the model is proved and analyzed to slack the constraints of AAM model parameters. Besides, a design procedure about solving AAM model parameters is given, and the face recognition method by AAM model is established, which includes image preprocessing, AAM model training, and image recognition. Finally, simulation results on two experiments show the feasibility and performance of the proposed face recognition method.

scholarly journals Biometrics Passport Authentication Using Facial Marks

2019 ◽  
Author(s):  
Ziaul Haque Choudhury
Keyword(s):  
Face Recognition ◽  
Recognition Accuracy ◽  
Personal Identification ◽  
Appearance Model ◽  
Active Shape Model ◽  
Face Database ◽  
Recognition Method ◽  
Shape Model ◽  
The Face ◽  
Canny Edge

A secure biometric passport in the field of personal identification for national security is proposed in this paper. This paper discusses about how to secure biometric passport by applying face recognition. Proper biometric features are unique for each individual and it is invariably in time, it is an unambiguous identifier of a person. But it may fail to authorize a person, if there are some changes in an applicant‘s appearance, such as a mustache, hair cut, and glasses, etc., the case of similar individuals like twins, siblings, similar faces or even doubles could head to individuality mismatch. Our proposed face recognition method is based on facial marks present in the face image to authenticate a person. We applied facial boundary detection purpose ASM (Active Shape Model) intoAAM (Active Appearance Model) using PCA (Principle Component Analysis). Facial marks are detected by applying Canny edge detector and HOG (Histogram Oriented Gradients). Experimental results reveal that our proposed method gives 94 percentage face recognition accuracy, using Indian face database from IIT, Kanpur.

An Algorithm of Face Recognition Feature Extraction Based on ICA

2013 ◽  
Vol 765-767 ◽  
pp. 2813-2816
Author(s):  
Ze Hua Zhou
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Facial Feature ◽  
Face Database ◽  
Recognition Method ◽  
Facial Feature Extraction ◽  
Extraction Algorithm ◽  
The Face ◽  
Key Issues ◽  
Detection And Recognition

Recently, automatic face recognition method has become one of the key issues in the field of pattern recognition and artificial intelligence. Typically, the face recognition process can be divided into three parts: the detection and recognition of human face, facial feature extraction and face recognition, and among which the facial feature extraction is the key to face recognition technology. In this paper, an extraction algorithm of face recognition feature, which is based on face recognition feature, is proposed. The experimental results based on the ORL face database demonstrate that this algorithm works well.

Bayesian Robust Coding Face Recognition Algorithm Based on New Dictionary

2014 ◽  
Vol 644-650 ◽  
pp. 4080-4083
Author(s):  
Ye Cai Guo ◽  
Ling Hua Zhang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Binary Image ◽  
Test Image ◽  
Face Database ◽  
Robust Coding ◽  
The Face ◽  
Isolated Points ◽  
Rate Comparison

In order to overcome the defects that the face recognition rate can be greatly reduced in the existing uncontrolled environments, Bayesian robust coding for face recognition based on new dictionary was proposed. In this proposed algorithm, firstly a binary image is gained by gray threshold transformation and a more clear image without some isolated points can be obtained via smoothing, secondly a new dictionary can be reconstructed via fusing the binary image with the original training dictionary, finally the test image can be classified as the existing class via Bayesian robust coding. The experimental results based on AR face database show that the proposed algorithm has higher face recognition rate comparison with RRC and RSC algorithm.

scholarly journals Face recognition attendance system using Local Binary Pattern (LBP)

2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Automated System ◽  
Student Attendance ◽  
Recognition Method ◽  
Face Recognition System ◽  
The Face

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.

A distributed face recognition method and performance optimization

2017 ◽  
Vol 25 (3) ◽  
pp. 779-785
Author(s):  
闵卫东 MIN Wei-dong ◽  
石 杰 SHI Jie ◽  
韩 清 HAN Qing ◽  
王 玮 WANG Wei
Keyword(s):  
Face Recognition ◽  
Performance Optimization ◽  
Recognition Method ◽  
And Performance

Stability and Performance of Virtual Reality-Based Telenanomanipulation System in SEM

2011 ◽  
Vol 183-185 ◽  
pp. 1746-1751 ◽  
Author(s):  
Dong Jie Li ◽  
Wei Bin Rong ◽  
Li Ning Sun ◽  
Wan Zhe Xiao
Keyword(s):  
Force Feedback ◽  
Design Procedure ◽  
Haptic Interface ◽  
Human Contact ◽  
Virtual Coupling ◽  
And Performance ◽  
The Stability ◽  
Carbon Nanowires ◽  
Measuring Force ◽  
Scanning Electron

In this paper, a master/slave telenanomanipulation control system with force feedback is established with the micro-positioner (Attocube) working in scanning electron microscope (SEM) as the slave side and the haptic device (Omega3) as the master side. An improved virtual coupling (IVC) algorithm is introduced based on nanoscale virtual coupling (NSVC) by adding a proportional- plus-integral (PI) velocity controller in the haptic interface. The stability and performance of the established system are discussed. This method leads to an explicit design procedure for virtual coupling networks which give greatest performance while guaranteeing stability both on moving carbon nanowires in SEM and measuring force at the point of device-human contact.

scholarly journals Boosting Face Recognition under Drastic Views Using a Pose AutoAugment Manner

2020 ◽  
Vol 10 (24) ◽  
pp. 8940
Author(s):  
Wanshun Gao ◽  
Xi Zhao ◽  
Jianhua Zou
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
The Third ◽  
Correlation Score ◽  
Face Matching ◽  
Invariant Feature ◽  
The Face ◽  
3D Databases ◽  
Model Training ◽  
Facial Images

Face recognition under drastic pose drops rapidly due to the limited samples during the model training. In this paper, we propose a pose-autoaugment face recognition framework (PAFR) based on the training of a Convolutional Neural Network (CNN) with multi-view face augmentation. The proposed framework consists of three parts: face augmentation, CNN training, and face matching. The face augmentation part is composed of pose autoaugment and background appending for increasing the pose variations of each subject. In the second part, we train a CNN model with the generated facial images to enhance the pose-invariant feature extraction. In the third part, we concatenate the feature vectors of each face and its horizontally flipped face from the trained CNN model to obtain a robust feature. The correlation score between the two faces is computed by the cosine similarity of their robust features. Comparable experiments are demonstrated on Bosphorus and CASIA-3D databases.

Face Recognition Based on Eigenface Image Reconstruction and Fisherface

2014 ◽  
Vol 1044-1045 ◽  
pp. 1153-1158
Author(s):  
Xiao Long Lu ◽  
Le Yang ◽  
Gang Cai ◽  
Zi Xing Mo ◽  
Li Peng
Keyword(s):  
Face Recognition ◽  
Image Reconstruction ◽  
Principal Component ◽  
Personal Characteristics ◽  
Face Database ◽  
Training Set ◽  
Recognition Method ◽  
Feature Subspace ◽  
Generating Matrix ◽  
Testing Set

In this paper, a new face recognition method based on eigenface image reconstruction and Fisherface is proposed, it is mainly used to reduce the loss of personal characteristics. First, we can obtain the feature subspace of all the classes in training set by using the inner-classes covariance matrix as generating matrix, and so we get the eigenfaces of each person (class). Next, we use the principal component of testing set, which is obtained by mapping testing set to the feature subspace, to reconstruct the testing images. Finally, we substitute the reconstructed testing images for the original ones, and then get the recognition work completed by using Fisherface method. The simulation illustrates the effectivity of the method on the ORL face database.

ILLUMINATION INVARIANT FACE RECOGNITION USING FABEMD DECOMPOSITION WITH DETAIL MEASURE WEIGHT

2011 ◽  
Vol 25 (08) ◽  
pp. 1261-1273 ◽  
Author(s):  
HENGXIN CHEN ◽  
Y. Y. TANG ◽  
BIN FANG ◽  
JING WEN
Keyword(s):  
Face Recognition ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Local Area ◽  
Face Database ◽  
Illumination Variation ◽  
Intrinsic Structure ◽  
Mode Decomposition ◽  
The Face ◽  
Bidimensional Empirical Mode Decomposition

With varying illumination conditions, facial features obtained from images are distorted nonlinearly by variant lighting intensity and direction, so face recognition becomes very difficult. According to the "common assumption", illumination varies slowly and the face intrinsic feature (including 3D surface and reflectance) varies rapidly in local area, we can then consider high frequency features that represent the face intrinsic structure. FABEMD8 (Fast and Adaptive Bidimensional Empirical Mode Decomposition) is a fast and adaptive method of BEMD22 (Bidimensional Empirical Mode Decomposition), and not using time-consuming plane interpolation computation, it can decompose the image into multilayer high frequency images representing detail features and low frequency images representing analogy features. But we cannot make a quantitative analysis of how many detail features can be used to eliminate illumination variation. So we propose two measures to quantify the detail features, and with these measure weights, we can activitate FABEMD based multilayer detail images matching for face recognition under varying illumination. With PCA, the experiments based on Yale face database B and MU PIE face database show that the method proposed in this paper can get remarkable performance.

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