Face Recognition System Based on PCA and Feedforward Neural Networks

Eleyan, Alaa; Demirel, Hasan

doi:10.1007/11494669_115

Cited by 26 publications

(10 citation statements)

References 9 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[28]. The eigenvectors are the ordered, that each one accounting for all the different amount of all the variation among all the face images [27]. …”

Section: Face Recognitionmentioning

confidence: 99%

A Hybrid Feature Extraction Technique for Face Recognition

Sharma¹,

Chhabra²

2017

IJARCSSE

View full text Add to dashboard Cite

Abstract-Pervasive work out is the expertise that is elegantly integrated in our everyday life. Form of home automation focuses on making it possible for elderly and for alone person to remain at home, safe and comfortable. The care of elderly could be enhanced through remote monitoring system, sensor technologies and communication systems. A form of home automation focuses on making it possible for elderly and individual people to remain at home safe and comfortable. Face Recognition is a concept under pattern recognition with applications moving towards the use of facial features for authorization and authentication. Hybrid approach has a special status among face recognition systems as they combine different feature extraction approaches to overcome the shortcomings of individual methods. This work uses Feed Forward Neural Network for face recognition and using a combination of PCA and ICA algorithm for feature extraction. Proposed algorithm is implemented in MATLAB and performance evaluation of success rate of proposed algorithm with existing techniques.Results show that proposed system has higher accuracy than face recognition using single feature extractor.

show abstract

“…[28]. The eigenvectors are the ordered, that each one accounting for all the different amount of all the variation among all the face images [27]. …”

Section: Face Recognitionmentioning

confidence: 99%

A Hybrid Feature Extraction Technique for Face Recognition

Sharma¹,

Chhabra²

2017

IJARCSSE

View full text Add to dashboard Cite

show abstract

“…Eleyan and Demirel [156] used principal components analysis to obtain feature projection vectors from face images which were then classified using feed forward neural networks. Some tests on the ORL database using various numbers of training and testing images showed that the performance of this system was better than the eigenfaces [80,81] one in which a nearest neighbor classifier was used for classification.…”

Section: Aimentioning

confidence: 99%

A Survey of Face Recognition Techniques

Jafri¹,

Arabnia²

2009

Journal of Information Processing Systems

563

269

View full text Add to dashboard Cite

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

show abstract

“…After a set of weights have been determined and the database initialized, the testing stage can accept a new face image, subject it to eigenface decomposition, and compare the resultant weights with the closest matched weights in the database to identify the identity of the input. Mathematical equations for PCA algorithm can be found in [8] and [10].…”

Section: Principal Components Analysmentioning

confidence: 99%

PCA-based face recognition from video using super-resolution

Al-Azzeh

Eleyan

Demirel

2008

2008 23rd International Symposium on Computer and Information Sciences

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a method to recognize faces from a set of consecutive video frames instead of a single image using super-resolution (SR). The SR process uses multiple frames acquired from video and combines information coming from them into a single image in higher resolution. As expected, a single low resolution image would contain less amount of information, than the same image taken from a video sequence with multiple other images with temporal changes from consecutive frames. the proposed method uses SR to generate a super resolved video sequences from a low resolution video sequences and uses frames acquired from the high resolution video sequences to train and test the performance of the principal component analysis based face recognition system. Entropy and MSE were used to check the performance of the system and the results showed the robustness of SR as a preprocessing step for recognition.

show abstract

Face Recognition System Based on PCA and Feedforward Neural Networks

Cited by 26 publications

References 9 publications

A Hybrid Feature Extraction Technique for Face Recognition

A Hybrid Feature Extraction Technique for Face Recognition

A Survey of Face Recognition Techniques

PCA-based face recognition from video using super-resolution

Contact Info

Product

Resources

About