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.

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 AN EXPERIMENTAL STUDY OF FACE RECOGNITION METHOD

2020 ◽  
Vol 1 (2) ◽  
pp. 52-58
Author(s):  
Paula Pereira ◽  
Tanara Kuhn
Keyword(s):  
Face Recognition ◽  
Wavelet Transformation ◽  
Euclidean Distance ◽  
Recognition Accuracy ◽  
Recognition Method ◽  
Training Images ◽  
Spectral Graph ◽  
Training Samples ◽  
The Face ◽  
Improved Methods

The increased use of face recognition techniques leads to the development of improved methods with higher accuracy and efficiency. Currently, there are various face recognition techniques based on different algorithm. In this study, a new method of face recognition is proposed based on the idea of wavelet operators for creating spectral graph wavelet transformation. The proposed idea relies on the spectral graph wavelet kernel procedure. In this proposed method, feature extraction is based on transformation into SGWT by means of spatial domain. For recognition purpose, the feature vectors are used for computation of selected training samples which makes the classification. The decomposition of face image is done using the SGWT. The system identifies the test image by calculating the Euclidean distance. Finally, the study conducted an experiment using the ORL face database. The result states that the recognition accuracy is higher in the proposed system which can be further improved using the number of training images. Overall, the result shows that the proposed method has good performance in terms of accuracy of the face recognition

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.

scholarly journals Neutral expression synthesis using kernel active shape model

2020 ◽  
Vol 20 (1) ◽  
pp. 150
Author(s):  
Marcella Peter ◽  
Jacey-Lynn Minoi ◽  
Suriani Ab Rahman
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Recognition Rate ◽  
Active Shape Model ◽  
Shape Model ◽  
Facial Identity ◽  
Modified Method ◽  
Active Shape ◽  
The Face ◽  
Recognition Systems

This paper presents a modified kernel-based Active Shape Model for neutralizing and synthesizing facial expressions. In recent decades, facial identity and emotional studies have gained interest from researchers, especially in the works of integrating human emotions and machine learning to improve the current lifestyle. It is known that facial expressions are often associated with face recognition systems with poor recognition rate. In this research, a method of a modified kernel-based active shape model based on statistical-based approach is introduced to synthesize neutral (neutralize) expressions from expressional faces, with the aim to improve the face recognition rate. An experimental study was conducted using 3D geometric facial datasets to evaluate the proposed modified method. The experimental results have shown a significant improvement on the recognition rates.

Computer Intelligent Nursing Care of Critically III Patients Based on Improved Patient Facial Expression Recognition Method

2014 ◽  
Vol 543-547 ◽  
pp. 2350-2353
Author(s):  
Xiao Yan Wan
Keyword(s):  
Facial Expression ◽  
Critically Ill ◽  
Nursing Care ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Appearance Model ◽  
Expression Recognition ◽  
Recognition Method ◽  
The Face

In order to extract the expression features of critically ill patients, and realize the computer intelligent nursing, an improved facial expression recognition method is proposed based on the of active appearance model, the support vector machine (SVM) for facial expression recognition is taken in research, and the face recognition model structure active appearance model is designed, and the attribute reduction algorithm of rough set affine transformation theory is introduced, and the invalid and redundant feature points are removed. The critically ill patient expressions are classified and recognized based on the support vector machine (SVM). The face image attitudes are adjusted, and the self-adaptive performance of facial expression recognition for the critical patient attitudes is improved. New method overcomes the effect of patient attitude to the recognition rate to a certain extent. The highest average recognition rate can be increased about 7%. The intelligent monitoring and nursing care of critically ill patients are realized with the computer vision effect. The nursing quality is enhanced, and it ensures the timely treatment of rescue.

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 Who_Is : Identification system of human faces

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Mohamed Tayeb Laskri ◽  
Djallel Chefrour
Keyword(s):  
Face Recognition ◽  
Geometric Model ◽  
Personal Identification ◽  
Identification System ◽  
K Nearest Neighbors ◽  
City Block ◽  
The Face ◽  
Characteristic Points ◽  
The Individual ◽  
Application Fields

International audience Although human face recognition is a hard topic due to many parameters involved (e.g. variability of the position, lighting, hairstyle, existence of glasses, beard, moustaches, wrinkles...), it becomes of increasing interest in numerous application fields (personal identification, video watch, man machine interfaces...). In this work, we present WHO_IS, a system for person identification based on face recognition. A geometric model of the face is definedfrom a set of characteristic points which are extracted from the face image. The identification consists in calculating the K nearest neighbors of the individual test by using the City-Block distance. The system is tested on a sample of 100 people with a success rate of 86 %. Bien que la reconnaissance des visages humains soit un domaine difficile à cause de la multitude des paramètres qu'il faut prendre en compte (variation de posture, éclairage, style de coiffure, port de lunettes, de barbes, de moustaches, vieillesse…), il est très important de s'en intéresser vu les nombreux champs d'applications (vérification de personnes, télésurveillance, interfaces homme-machine …). Dans ce travail nous présentons la mise en œuvre de WHO_IS, un système d'identification de personnes par reconnaissance des visages humains. Nous avons développé un modèle géométrique du visage basé sur un ensemble de points caractéristiques extraits à partir de l'image du visage. La procédure d'identification consiste à calculer les K plus proches voisins de l'individu test dans le sens de la distance City-Block. Le système WHO_IS a été testé sur un échantillon de 100 personnes. Un taux de reconnaissance correcte de 86% a été obtenu

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