Human Ear Recognition Using Hybrid Filter and Supervised Locality Preserving Projection

2012 ◽  
Vol 529 ◽  
pp. 271-275
Author(s):  
Song Ze Lei ◽  
Qiang Zhu
Keyword(s):  
Gabor Filter ◽  
Recognition Rate ◽  
Difficult Problem ◽  
Hybrid Filter ◽  
Locality Preserving Projection ◽  
Ear Recognition ◽  
Locality Preserving ◽  
Discriminative Feature ◽  
Human Ear ◽  
Log Gabor

To solve the difficult problem of human ear recognition caused by variety of ear angle, a novel method which combines hybrid filter with supervised locality preserving projection (SLPP) is proposed. The ear image is firstly filtered by Log-Gabor filter which is constructed with 5 scales and 8 orientations. The important parameters of Log-Gabor filter are selected through experiments. To form effective and discriminative feature too many Log-Gabor coefficients are reduced by discrete cosine transform. Lastly feature is constructed by SLPP to discovery geometrical rules. Experimental results show that compared with the traditional methods, the proposed method obtains higher recognition rate, and is robust to multi-pose of ear recognition.

Human Ear Recognition Based on Phase Congruency and Kernel Discriminant Analysis

2012 ◽  
Vol 241-244 ◽  
pp. 1614-1617
Author(s):  
Song Ze Lei ◽  
Qiang Zhu
Keyword(s):  
Discriminant Analysis ◽  
Gabor Filter ◽  
Recognition Rate ◽  
High Dimensional ◽  
Dimensional Vector ◽  
Phase Congruency ◽  
Kernel Discriminant Analysis ◽  
Ear Recognition ◽  
Novel Method ◽  
Human Ear

To solve the multi-pose ear recognition problem under the different illumination condition, a novel method which combines phase congruency with kernel discriminant analysis (KDA) is proposed. The phase congruency of ear image is first calculated using Log-Gabor filter with 5 scales and 8 orientations, and then the phase congruency of different orientation is constructed as high dimensional vector including ample information. The high dimensional vector is mapped to kernel space to acquire discriminant feature. Experimental results show that the proposed method obtains higher recognition rate compared with the other related methods. The method of the phase congruency can eliminate the influence of illumination and phase congruency with KDA is effective to multi-pose ear recognition.

Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

2010 ◽  
Vol 121-122 ◽  
pp. 391-398 ◽  
Author(s):  
Qi Rong Zhang ◽  
Zhong Shi He
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Image Feature ◽  
Two Dimensional ◽  
Face Database ◽  
Image Feature Extraction ◽  
Locality Preserving Projection ◽  
Locality Preserving

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

Face recognition algorithm based on Gabor wavelet and locality preserving projections

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740041 ◽  
Author(s):  
Xiaojie Liu ◽  
Lin Shen ◽  
Honghui Fan
Keyword(s):  
Face Recognition ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Gabor Wavelet ◽  
Light Illumination ◽  
Locality Preserving Projections ◽  
Locality Preserving ◽  
Illumination Changes ◽  
The Face

In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.

scholarly journals An ear recognition system based on local wavelet subband energy distribution

2021 ◽  
Vol 24 (2) ◽  
pp. 1212
Author(s):  
Ruaa Isam Fadhil ◽  
Loay E. George
Keyword(s):  
Energy Distribution ◽  
Recognition Rate ◽  
Low Frequency ◽  
Recognition System ◽  
Individual Identification ◽  
Full Description ◽  
Ear Recognition ◽  
Biometric Characteristic ◽  
Human Ear ◽  
Selection Of

The outer ear features have been used for many years in forensic science of recognition. Human ear is a valuable information provenance of data for individual identification/authentication. Ear meets biometric characteristic (universality, distinctiveness, permanence and collectability). Biometric system depending on ear image facing two major challenges; the first one is the localization of human ear area in given profile face image, and the second one is the selection of proper features to distinguish between individuals. In this work, we propose an alogorithm for ear recognition based on the local spatial energy distribution of wavelet sub-bands, because of wavelet transform has the ability to analyze the local feature of 2-D image by determining where the low frequency and high frequency areas are and it provides full description of the spatial distribution of the ear image. Nearest classifier are used to make a recognition decision in matching stage. The system was tested over a public database consist of 493 images. The attained recognition rate was (95.28%) and the achieved minimum equal error rate (EER) is 0.02%.

Ear Recognition Based on Point Feature

2013 ◽  
Vol 380-384 ◽  
pp. 3840-3845
Author(s):  
Ying Tian ◽  
De Bin Zhang
Keyword(s):  
Force Field ◽  
Recognition Rate ◽  
Image Resolution ◽  
Feature Points ◽  
Transformation Theory ◽  
Scale Invariant ◽  
Ear Recognition ◽  
Field Transformation ◽  
Human Ear ◽  
Point Feature

In order to improve recognition rate of human ear, a method based on point feature of image for ear recognition is proposed in this paper. Firstly force field transformation theory is applied to human ear image two times in our method. It can extract the structural feature points and contour feature points of ear respectively and compose feature point set. Then feature points described by the scale invariant feature transformation descriptor. At last nearest neighbor classifier is employed for ear recognition. Feature points extracted from ear image using force field transformation are stable, reliable and discriminative, and they are insensitive to variations in image resolution. Constructing descriptor can resolve the problems caused by lower recognition owing to illumination change, scaling transformation, rotation and minute alteration caused by pose transformation. The experimental results show that the proposed algorithm not only can effectively improve ear recognition rate but also has quite good robustness.

scholarly journals A Synthetic Fusion Rule Based on FLDA and PCA for Iris Recognition Using 1D Log-Gabor Filter

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Rachida Tobji ◽  
Wu Di ◽  
Naeem Ayoub
Keyword(s):  
Iris Recognition ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Principal Component ◽  
Fusion Rule ◽  
Biometric Recognition ◽  
Pca Method ◽  
Recognition Systems ◽  
Iris Patterns ◽  
Log Gabor

Iris recognition is one of the most useful methods to identify or verify people in biometric recognition systems. Iris patterns contain many features that distinguish people from each other. In this paper, a novel iris recognition method is proposed based on the fusion of Fisher Linear Discriminate Analysis (FLDA) with embedding Principal Component Analysis (PCA) method. In this work, firstly we use 1D Log-Gabor to elicit the iris features from an approximation part. Secondly, we obtain an appropriate degree of clarity for the iris with fusion of FLDA/PCA to eliminate the optical reflections on the iris image. Experiments of our proposed algorithm are performed on the CASIA V1 database. The results of our proposed approach show a good performance with recognition rate up to 99.99%.

Sign in / Sign up

Export Citation Format

Share Document