Cosmetic applied based face recognition for biometric passport

2019 ◽  
Vol 8 (1) ◽  
pp. 3-22
Author(s):  
Ziaul Haque Choudhury ◽  
M. Munir Ahamed Rabbani
Keyword(s):  
Face Recognition ◽  
National Security ◽  
International Crime ◽  
Principal Component ◽  
Personal Identification ◽  
Gradient Field ◽  
Appearance Model ◽  
Biometric Authentication ◽  
Content Type ◽  
Verification Process

Purpose Nowadays, the use of forged e-passport is increasing, which is threatening national security. It is important to improve the national security against international crime or terrorism. There is a weak verification process caused by lack of identification processes such as a physical check, biometric check and electronic check. The e-passport can prevent the passport cloning or forging resulting from the illegal immigration. The paper aims to discuss these issues. Design/methodology/approach This paper focuses on face recognition to improve the biometric authentication for an e-passport, and it also introduces facial permanent mark detection from the makeup or cosmetic-applied faces, twins and similar faces. An algorithm is proposed to detect the cosmetic-applied facial permanent marks such as mole, freckle, birthmark and pockmark. Active Shape Model into Active Appearance Model using Principal Component Analysis is applied to detect the facial landmarks. Facial permanent marks are detected by applying the Canny edge detector and Gradient Field Histogram of Oriented Gradient. Findings This paper demonstrated an algorithm and proposed facial marks detection from cosmetic or makeup-applied faces for a secure biometric passport in the field of personal identification for national security. It also presented to detect and identify identical twins and similar faces. This paper presented facial marks detection from the cosmetic-applied face, which can be mixed with traditional methods. However, the use of the proposed technique faced some challenges due to the use of cosmetic. The combinations of the algorithm for facial mark recognition matching with classical methods were able to attain lower errors in this proposed experiment. Originality/value The proposed method will enhance the national security and it will improve the biometric authentication for the e-passport. The proposed algorithm is capable of identifying facial marks from cosmetic-applied faces accurately, with less false positives. The proposed technique shows the best results.

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

2013 ◽  
Vol 5 (4) ◽  
pp. 82-94 ◽  
Author(s):  
A. F. M. Saifuddin Saif ◽  
Anton Satria Prabuwono ◽  
Zainal Rasyid Mahayuddin ◽  
Teddy Mantoro
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Personal Identification ◽  
Human Face ◽  
Analysis Model ◽  
Human Face Recognition ◽  
Proposed Model ◽  
The Face

Face recognition has been used in various applications where personal identification is required. Other methods of person's identification and verification such as iris scan and finger print scan require high quality and costly equipment. The objective of this research is to present an extended principal component analysis model to recognize a person by comparing the characteristics of the face to those of new individuals for different dimension of face image. The main focus of this research is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background is constant. This research requires a normal camera giving a 2-D frontal image of the person that will be used for the process of the human face recognition. An Extended Principal Component Analysis (EPCA) technique has been used in the proposed model of face recognition. Based on the experimental results it is expected that proposed the EPCA performs well for different face images when a huge number of training images increases computation complexity in the database.

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 Citizen Unique ID Based Real Time Face Recognition for Surveillance and National Security

2020 ◽  
Vol 8 (6) ◽  
pp. 1830-1834
Keyword(s):  
Face Recognition ◽  
National Security ◽  
Motion Detection ◽  
Principal Component ◽  
Single System ◽  
Security Services ◽  
Key Innovation ◽  
The Face ◽  
Adaptive Principal Component Analysis ◽  
Better Than

Provision of home security services has become an integral part of our lives in today’s technological society where attackers usually have all the necessary means and resources at their disposal. Face Recognition is producing gigantic enthusiasm because of government worries about character the executives and worldwide fear based oppressor movement. One aspiration of Intelligent CCTV is to help counteract fear based oppression and a key innovation is solid face acknowledgment. The movement discovery module is dependable to decide the degree of action while the face identification module separates between approved individuals and interlopers. Our system is better than many proposed systems as it combines both motion detection and face recognition in a single system. Our framework has three noteworthy segments containing: 1) a Viola-Jones face discovery module 2) a Pose Normalization Module to evaluate facial posture and make up for extraordinary posture points 3) Adaptive Principal Component Analysis to perceive the standardized appearances.

A new magnetic resonance head coil and head immobilization device for gamma knife radiosurgery: an analysis of geometric distortion and signal/noise characteristics

2002 ◽  
Vol 97 ◽  
pp. 563-568 ◽  
Author(s):  
Paul Jursinic ◽  
Robert Prost ◽  
Christopher Schultz
Keyword(s):  
Magnetic Resonance ◽  
Gamma Knife Radiosurgery ◽  
Geometric Distortion ◽  
Gradient Field ◽  
Main Magnetic Field ◽  
Content Type ◽  
Signal Noise ◽  
Noise Characteristics ◽  
Head Coil ◽  
Fine Print

Object. The authors report on a new head coil into which the Leksell aluminum localization frame can be easily and securely mounted. Mechanically, the head coil interferes little with the patient. Methods. The head coil, which is for magnetic resonance (MR) imaging, is a 12-element quadrature transmitand-receive high-pass birdcage coil with a nominal operation frequency (63.86 MHz). The coil was built into a plastic housing. This new head coil minimizes patient motion and provides a 20% increase in signal/noise ratios compared with standard head coils. An MR image test phantom was mounted in the coil and this allowed quantification of image distortion due to inhomogeneities in the main magnetic field, nonlinearity in the gradient field, and paramagnetism of the aluminum headframe. There were no significant differences in geometric distortion between the new head coil and the standard coil. Conclusions. The new head coil has advantages for reducing patient movement artifacts and has a better signal/noise ratio with no reduction in geometric accuracy.

Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

2014 ◽  
Vol 41 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Suyong Yeon ◽  
ChangHyun Jun ◽  
Hyunga Choi ◽  
Jaehyeon Kang ◽  
Youngmok Yun ◽  
...  
Keyword(s):  
Principal Component ◽  
Point Clouds ◽  
Previous Method ◽  
Divide And Conquer ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Content Type ◽  
Extraction Algorithm ◽  
Plane Extraction ◽  
Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

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