A Robust Image Preprocessing Algorithm for Face Recognition

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.

Feature Representation Method Based on Kirsch Masks Filter for Face Recognition

2014 ◽  
Vol 989-994 ◽  
pp. 4209-4212
Author(s):  
Zhao Kui Li ◽  
Yan Wang
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Feature Representation ◽  
Linear Discriminant ◽  
Robust Coding ◽  
Gabor Features ◽  
Robust Face Recognition ◽  
Representation Method ◽  
Low Dimensional ◽  
Direction Image

In this paper, a feature representation method based on Kirsch masks filter for face recognition is proposed. We firstly obtain eight direction images by performing Kirsch masks filter. For each direction image, the low-dimensional feature vector is computed by Linear Discriminant Analysisis. Then, a fusion strategy is used to combine different direction image according to their respective salience. Experimental results show that our methods significantly outperform popular methods such as Gabor features, Local Binary Patterns, Regularized Robust Coding (RRC), and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

A Robust Face Representation Method for Face Recognition

2014 ◽  
Vol 945-949 ◽  
pp. 1801-1804
Author(s):  
Zhao Kui Li
Keyword(s):  
Face Recognition ◽  
Feature Vector ◽  
State Of The Art ◽  
Linear Discriminant ◽  
Face Representation ◽  
Spatial Locality ◽  
Score Method ◽  
Robust Face Recognition ◽  
Representation Method ◽  
Low Dimensional

In this paper, a robust face representation method based on multiple gradient orientations for face recognition is proposed. We introduce multiple gradient orientations and compute multiple orientation images which display different spatial locality and orientation properties. Each orientation image is normalized using the “z-score” method, and all normalized vectors are concatenated into an augmented feature vector. The dimensionality of the augmented feature vector is reduced by linear discriminant analysis to yield a low-dimensional feature vector. Experimental results show that our method achieves state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

SUBSPACE LEARNING BASED ON LAPLACIAN EIGENMAPS AND LDA FOR FACE RECOGNITION

2006 ◽  
Vol 03 (01) ◽  
pp. 45-51
Author(s):  
YANWEI PANG ◽  
ZHENGKAI LIU ◽  
YUEFANG SUN
Keyword(s):  
Face Recognition ◽  
Subspace Learning ◽  
Dimensional Subspace ◽  
Training Data ◽  
Objective Functions ◽  
Recognition Method ◽  
Laplacian Eigenmaps ◽  
Linear Discriminant ◽  
Structure Information ◽  
Low Dimensional

Subspace-based face recognition method aims to find a low-dimensional subspace of face appearance embedded in a high-dimensional image space. The differences between different methods lie in their different motivations and objective functions. The objective function of the proposed method is formed by combining the ideas of linear Laplacian eigenmaps and linear discriminant analysis. The actual computation of the subspace reduces to a maximum eigenvalue problem. Major advantage of the proposed method over traditional methods is that it utilizes both local manifold structure information and discriminant information of the training data. Experimental results on the AR face databases demonstrate the effectiveness of the proposed method.

FACE REPRESENTATION WITH GRADIENT ORIENTATIONS AND EULER MAPPING: APPLICATION TO FACE RECOGNITION

2014 ◽  
Vol 28 (08) ◽  
pp. 1456014 ◽  
Author(s):  
ZHAOKUI LI ◽  
LIXIN DING ◽  
YAN WANG ◽  
JINRONG HE
Keyword(s):  
Face Recognition ◽  
Imaginary Part ◽  
Feature Vector ◽  
Low Frequency ◽  
Subspace Learning ◽  
The Real ◽  
Face Representation ◽  
Central Pixel ◽  
Low Dimensional ◽  
Two Stages

This paper proposes a simple, yet very powerful local face representation, called the Gradient Orientations and Euler Mapping (GOEM). GOEM consists of two stages: gradient orientations and Euler mapping. In the first stage, we calculate gradient orientations of a central pixel and get the corresponding orientation representations by performing convolution operator. These representation results display spatial locality and orientation properties. To encompass different spatial localities and orientations, we concatenate all these representation results and derive a concatenated orientation feature vector. In the second stage, we define an explicit Euler mapping which maps the space of the concatenated orientation into a complex space. For a mapping image, we find that the imaginary part and the real part characterize the high frequency and the low frequency components, respectively. To encompass different frequencies, we concatenate the imaginary part and the real part and derive a concatenated mapping feature vector. For a given image, we use the two stages to construct a GOEM image and derive an augmented feature vector which resides in a space of very high dimensionality. In order to derive low-dimensional feature vector, we present a class of GOEM-based kernel subspace learning methods for face recognition. These methods, which are robust to changes in occlusion and illumination, apply the kernel subspace learning model with explicit Euler mapping to an augmented feature vector derived from the GOEM representation of face images. Experimental results show that our methods significantly outperform popular methods and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

scholarly journals Face Recognition and Drunk Classification Using Infrared Face Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Gabriel Hermosilla ◽  
José Luis Verdugo ◽  
Gonzalo Farias ◽  
Esteban Vera ◽  
Francisco Pizarro ◽  
...  
Keyword(s):  
Face Recognition ◽  
Promising Result ◽  
Gaussian Mixture ◽  
Local Descriptor ◽  
Linear Discriminant ◽  
Face Images ◽  
The Face ◽  
Robust Face Recognition ◽  
Thermal Face ◽  
Two Stages

The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifier based on a Gaussian mixture model, creating a classification space for each person, extending the state-of-the-art concept of a “DrunkSpace Classifier.” The system was validated using a new drunk person database, which was specially designed for this work. The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for our database in comparison with others that can be found in the literature.

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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