Human Face Recognition and the Face Image Set′s Topology

1994 ◽  
Vol 59 (2) ◽  
pp. 254-261 ◽  
Author(s):  
M. Bichsel ◽  
A.P. Pentland
Keyword(s):  
Face Recognition ◽  
Face Image ◽  
Human Face ◽  
Human Face Recognition ◽  
The Face ◽  
Image Set

scholarly journals FACE RECOGNITION THROUGH CASCADE CLASSIFIER USING EIGENFACES ALGORITHMS.

2020 ◽  
Vol 9 (6) ◽  
pp. 39-47
Keyword(s):  
Face Recognition ◽  
Dimensional Space ◽  
Principal Component ◽  
Face Image ◽  
Human Face ◽  
Cascade Classifier ◽  
Facial Image ◽  
Human Face Recognition ◽  
Cascade Classifiers ◽  
The Face

The easiest way to distinguish each person's identity is through the face. Face recognition is included as an inevitable pre-processing step for face recognition. Face recognition itself has to face difficulties and challenges because sometimes some form of issue is quite different from human face recognition. There are two stages used for the human face recognition process, i.e. face detection, where this process is very fast in humans. In the first phase, the person stored the face image in the database from a different angle. The person's face image storage with the help of Eigenvector value depended on components - face coordinates, face index, face angles, eyes, nose, lips, and mouth within certain distances and positions with each other. There are two types of methods that are popular in currently developed face recognition patterns, the Cascade Classifier method and the Eigenface Algorithm. Facial image recognition The Eigenface method is based on the lack of dimensional space of the face, using principal component analysis for facial features. The main purpose of the use of cascade classifiers on facial recognition using the Eigenface Algorithm was made by finding the eigenvectors corresponding to the largest eigenvalues of the facial image

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.

Face Recognition Based on Wavelet Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 216-219
Author(s):  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
The Face ◽  
Simulation Testing

It's well known that the technology of human face recognition has become a hot topicin pattern recognition field. Though a lot of progress has been made by many researchersthese years, many key problems still have to be solved in order to popularize the application of face recognition because of the complexity of face recognition. The background, development and main methods of face recognition are introducedfirstly in this paper, then a face recognition method which is based on wavelet transform,KL transform and BP neural networks is used in the paper.Here the face feature extraction includes wavelet transform and KL transform.Moreover,the recognition classifier is BP neural networks.The simulation testing in the paper holds good recognition rate.

scholarly journals Face Image Set Recognition Based On Improved HOG-NMF and Convolutional Neural Networks

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Lixiu Hao ◽  
Weiwei Yu
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Adverse Effects ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Weight Coefficient ◽  
Face Image ◽  
Segmented Images ◽  
The Face ◽  
Image Set

Objective Face recognition can be affected by unfavorable factors such as illumination, posture and expression, but the face image set is a collection of people’s various angles, different illuminations and even different expressions, which can effectively reduce these adverse effects and get higher face recognition rate. In order to make the face image set have higher recognition rate, a new method of combining face image set recognition is proposed, which combines an improved Histogram of Oriented Gradient (HOG) feature and Convolutional Neural Network (CNN). Method The method firstly segments the face images to be identified and performs HOG to extract features of the segmented images. Secondly, calculate the information entropy contained in each block as a weight coefficient of each block to form a new HOG features, and non-negative matrix factorization (NMF) is applied to reduce HOG features. Then the reduced-dimensional HOG features are modeled as image sets which keep your face details as much as possible. Finally, the modeled image sets are classified by using a convolutional neural network. Result The experimental results show that compared with the simple CNN method and the HOG-CNN method, the recognition rate of the method on the CMU PIE face set is increased by about 4%~10%. Conclusion The method proposed in this paper has more details of the face, overcomes the adverse effects, and improves the accuracy.

Human Face Recognition Based on the Core Characteristics under the Condition of Illumination

2014 ◽  
Vol 484-485 ◽  
pp. 991-995
Author(s):  
Wen Hui Li ◽  
Ning Ma ◽  
Zhi Yan Wang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Face Image ◽  
Experimental Result ◽  
Support Vector ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
The Core ◽  
Core Characteristics

A core characteristics based human face recognition method under the condition of illumination is proposed according to the problem of the sharply declining human face recognition rate under the condition of light. With this method, if human face image is affected by light and the illumination is forward or side can be judged; the images affect by illumination are processed using the strategy of frequency domain replacement, and then the key areas of human face image are divided and then are recognized using support vector machine (SVM) based on the unit of area, and finally the recognition results are integrated. The experimental result shows that this method can produce a better recognition effect than other methods in view of the problem of illumination.

A LOW-COMPUTATION APPROACH FOR HUMAN FACE RECOGNITION

2012 ◽  
Vol 26 (06) ◽  
pp. 1256015 ◽  
Author(s):  
WEI-LI FANG ◽  
YING-KUEI YANG ◽  
JUNG-KUEI PAN
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Face Image ◽  
Experimental Results ◽  
Human Face ◽  
Human Face Recognition ◽  
Computation Cost ◽  
Recognition Algorithms

Several 2DPCA-based face recognition algorithms have been proposed hoping to achieve the goal of improving recognition rate while mostly at the expense of computation cost. In this paper, an approach named SI2DPCA is proposed to not only reduce the computation cost but also increase recognition performance at the same time. The approach divides a whole face image into smaller sub-images to increase the weight of features for better feature extraction. Meanwhile, the computation cost that mainly comes from the heavy and complicated operations against matrices is reduced due to the smaller size of sub-images. The reduced amount of computation has been analyzed and the integrity of sub-images has been discussed thoroughly in the paper. The experiments have been conducted to make comparisons among several better-known approaches and SI2DPCA. The experimental results have demonstrated that the proposed approach works well on reaching the goals of reducing computation cost and improving recognition performance simultaneously.

FACE DETECTION AND RESTORATION UNDER OCCLUSIONS : A LITERATURE REVIEW

2018 ◽  
Author(s):  
Smitha Lingadahalli Ravi
Keyword(s):  
Face Recognition ◽  
Expansion Method ◽  
Singular Value ◽  
Human Face ◽  
Security Systems ◽  
Human Face Recognition ◽  
The Face ◽  
Feature Based ◽  
Value Decomposition ◽  
Method Model

One of the main challenges of the face recognition is occlusions. Occlusion in an image refers to hindrance in the view of an object. Face recognition which work in the presence of occlusions are core for the current needs in the field of identification of suspects, as criminals try to take advantage of the weaknesses among the implemented security systems by camouflaging themselves and occluding their face with eyeglasses, hair, hands, or covering their face with scarves and hats. Human face recognition under occlusion is broadly classified into 8 categories Karhunen-Loeve Expansion Method, Model Based Method, Correlation Based Method, Template Based Method, Feature Based Method, Geometric Based Method, Singular Value Decomposition Based Method and Neural Network Based Algorithm. The paper details the experiments and databases used to handle the problem of occlusion and the results obtained by different authors. Lastly, a comparison of various techniques is presented and some conclusions are drawn referring to the best outcomes.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

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