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.

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.

A Image Decomposition Method Based on Euler Mapping for Face Recognition

2014 ◽  
Vol 989-994 ◽  
pp. 4119-4122
Author(s):  
Zhao Kui Li ◽  
Yan Wang
Keyword(s):  
Feature Vector ◽  
State Of The Art ◽  
Image Decomposition ◽  
Feature Representation ◽  
Image Feature ◽  
Z Score ◽  
Linear Discriminant ◽  
Score Method ◽  
Representation Method ◽  
Low Dimensional

This paper presents a robust but simple image feature representation method, called image decomposition based on Euler mapping (IDEM). IDEM firstly captures the orientation information by implementing arctangent operator for each pixel. Then, the orientation image is decomposed into two mapping images by executing Euler mapping. Each mapping 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 IDEM achieves better results in comparison with state-of-the-art methods.

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.

Transform Based Feature Extraction and Dimensionality Reduction Techniques

2010 ◽  
pp. 120-136
Author(s):  
Dattatray V. Jadhav ◽  
V. Jadhav Dattatray ◽  
Raghunath S. Holambe ◽  
S. Holambe Raghunath
Keyword(s):  
Face Recognition ◽  
Radon Transform ◽  
Low Frequency ◽  
Recognition System ◽  
Feature Representation ◽  
Discrete Wavelet ◽  
Plane Rotation ◽  
Discrimination Power ◽  
Reduction Techniques ◽  
Low Dimensional

Various changes in illumination, expression, viewpoint, and plane rotation present challenges to face recognition. Low dimensional feature representation with enhanced discrimination power is of paramount importance to face recognition system. This chapter presents transform based techniques for extraction of efficient and effective features to solve some of the challenges in face recognition. The techniques are based on the combination of Radon transform, Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT). The property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. The comparative study of various transform based techniques under different conditions like varying illumination, changing facial expressions, and in-plane rotation is presented in this chapter. The experimental results using FERET, ORL, and Yale databases are also presented in the chapter.

Illumination and Expression Invariant Face Recognition

2017 ◽  
Vol 31 (12) ◽  
pp. 1756018 ◽  
Author(s):  
Manasi Dhekane ◽  
Ayan Seal ◽  
Pritee Khanna
Keyword(s):  
Face Recognition ◽  
Feature Vector ◽  
Recognition Rate ◽  
Local Binary Patterns ◽  
Feature Representation ◽  
Computational Time ◽  
Texture Image ◽  
K Nearest Neighbor ◽  
Structure Information ◽  
Legendre Moments

An illumination and expression invariant face recognition method based on uniform local binary patterns (uLBP) and Legendre moments is proposed in this work. The proposed method exploits uLBP texture features and Legendre moments to make a feature representation with enhanced discriminating power. The input images are preprocessed to extract the face region and normalized. From normalized image, uLBP codes are extracted to obtain texture image which overcomes the effect of monotonic temperature changes. Legendre moments are computed from this texture image to get the required feature vector. Legendre moments conserve the spatial structure information of the texture image. The resultant feature vector is classified using k-nearest neighbor classifier with [Formula: see text] norm. To evaluate the proposed method, experiments are performed on IRIS and NVIE databases. The proposed method is tested on both visible and infrared images under different illumination and expression variations and performance is compared with recently published methods in terms of recognition rate, recall, length of feature vector, and computational time. The proposed method gives better recognition rates and outperforms other recent face recognition methods.

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.

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