Design and Research of Face Recognition Simulation System Based on Image Preprocessing

Various poses, facial expressions and illuminations are the biggest challenges in the fields of face recognition. To overcome these challenges, we propose a simple yet novel method in this paper by using the approximately symmetrical virtual face. Firstly, based on the symmetrical characteristics of faces, we present the method to generate the virtual faces for all samples in detail. Secondly, the collaborative representation based classification method is performed on both of the original set and virtual set individually. In this way, two kinds of representation residuals of every class can be obtained. Thirdly, an adaptive weighted fusion approach is presented and utilized to integrate those two kinds of representation residuals for face recognition. Lastly, we can obtain the label of the test sample by assigning it to the class with the minimum fused residual. Several experiments are conducted which show that the proposed method not only can greatly improve the classification accuracy, but also can effectively reduce the negative influence of various poses, illuminations, and facial expressions.

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Statistical face image preprocessing and non-statistical face representation for practical face recognition

2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) ◽

10.1109/isspit.2009.5407525 ◽

2009 ◽

Cited By ~ 2

Author(s):

Bongjin Jun ◽

Hyung-Soo Lee ◽

Jinseok Lee ◽

Daijin Kimy

Keyword(s):

Face Recognition ◽

Face Image ◽

Image Preprocessing ◽

Face Representation

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A Robust Image Preprocessing Algorithm for Face Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.4205 ◽

2014 ◽

Vol 989-994 ◽

pp. 4205-4208

Author(s):

Yan Wang ◽

Zhao Kui Li

Keyword(s):

Face Recognition ◽

Image Preprocessing ◽

Linear Discriminant ◽

Central Pixel ◽

Local Window ◽

Robust Image ◽

Robust Face Recognition ◽

Low Dimensional ◽

Arctangent Function ◽

A Current

In order to obtain more robust face recognition results, the paper proposes an image preprocessing method based on average gradient angle (AGA). It is based on the fact that the central pixel and its neighbors are similar in the local window of an image. AGA firstly calculates the ratio between the relative intensity differences of a current pixel against its neighbors and the number of its neighbors, then employs the arctangent function on the ratio. The dimensionality of the AGA image is reduced by linear discriminant analysis to yield a low-dimensional feature vector. Experimental results show that the proposed method achieves more robust results in comparison with state-of-the-art methods in AR face database.

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Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.1.12 ◽

2017 ◽

Vol 13 (1) ◽

pp. 104-113

Author(s):

Yaqeen Mezaal

Keyword(s):

Face Recognition ◽

Recognition Rate ◽

Recognition System ◽

Input Image ◽

Birth Date ◽

Image Preprocessing ◽

Face Recognition System ◽

Proposed Model ◽

Occurrence Matrix ◽

Local Average

Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images.

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