Face Recognition Use Local Image Dataset and Correlation Technique

Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

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DWT-based Illumination Normalization and Feature Extraction for Enhanced Face Recognition

International Journal of Engineering & Technology ◽

10.14419/ijet.v1i4.353 ◽

2012 ◽

Vol 1 (4) ◽

pp. 483 ◽

Cited By ~ 11

Author(s):

Manikantan K ◽

Milan S Shet ◽

Minal Patel ◽

Ramachandran S

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Recognition Rate ◽

Substantial Reduction ◽

Feature Space ◽

Binary Particle Swarm Optimization ◽

Discrete Wavelet ◽

Feature Subset ◽

Lighting Conditions ◽

Optimal Feature Subset

Face recognition (FR) under varying lighting conditions is challenging, and exacting illumination invariant features is an effective approach to solve this problem. In this paper, we propose to utilize Discrete Wavelet Transform (DWT) for normalizing the illumination variance in images as well as for feature extraction. Individual stages of the FR system are examined and an attempt is made to improve each stage. A Binary Particle Swarm Optimization (BPSO) based feature selection algorithm is used to search the feature space for the optimal feature subset. Experimental results, obtained by applying the proposed algorithm on YaleB and Color FERET face databases, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and a substantial reduction in the number of features is observed. Dimensionality reduction obtained is more than 99% for both YaleB and Color FERET databases.

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Signature Recognition Based on Discrete Wavelet Transform

UHD Journal of Science and Technology ◽

10.21928/uhdjst.v3n1y2019.pp19-29 ◽

2019 ◽

Vol 3 (1) ◽

pp. 19

Author(s):

Sivana Salahadin Muhamad ◽

Muzhir Shaban Al-Ani

Keyword(s):

Feature Extraction ◽

Wavelet Transforms ◽

Recognition Rate ◽

Personal Identification ◽

Discrete Wavelet ◽

Discrete Wavelet Transforms ◽

Human Identity ◽

Processing Stage ◽

Signature Recognition ◽

Developing Area

Personal identification is an actively developing area of research. Human signature is a vital biometric attribute which can be used to authenticate human identity. There are many approaches to recognize signature with a lot of researches. The aim of this research is to introduce an efficient approach for signature recognition. This approach starts with the process the acquired signatures and stores these signatures in the database to be ready for verification. The collection of signature data based on collecting samples of 10 people and 10 signatures for each person through traditional ink stamp method. These signatures are digitized to be ready for processing. Many steps are applied to the acquired images to perform the pre-processing stage. The proposed approach based on discrete wavelet transforms to extract significant features from each signature image. Pre-processing is applied at the beginning of this approach to avoid any unwanted noise. This approach consists of many steps: Data acquisition, pre-processing, signature registration, and feature extraction. High recognition rate results (100%) are obtained through applying this approach.

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Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

Recent Patents on Engineering ◽

10.2174/1872212112666180601085544 ◽

2019 ◽

Vol 13 (2) ◽

pp. 136-141 ◽

Cited By ~ 2

Author(s):

Abhisek Sethy ◽

Prashanta Kumar Patra ◽

Deepak Ranjan Nayak

Keyword(s):

Feature Extraction ◽

Random Forest ◽

Character Recognition ◽

Recognition Rate ◽

Discrete Wavelet ◽

Gray Level ◽

Handwritten Character Recognition ◽

Handwritten Character ◽

Wide Range ◽

Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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Kernel Null Space Marginal Fisher Analysis for Face Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.889-890.1065 ◽

2014 ◽

Vol 889-890 ◽

pp. 1065-1068

Author(s):

Yu’e Lin ◽

Xing Zhu Liang ◽

Hua Ping Zhou

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Sample Size ◽

High Performance ◽

Null Space ◽

Small Sample Size ◽

Recognition Rate ◽

Original Data ◽

Small Sample ◽

Neighborhood Structure

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is presented, which means that all the discriminant vectors can be calculated in the null space of the within-class scatter. KNSMFA not only exploits the nonlinear features but also overcomes the small sample size problems. Experimental results on ORL database indicate that the proposed method achieves higher recognition rate than the MFA method and some existing kernel feature extraction algorithms.

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Exploring the Uncertainty Space of Ensemble Classifiers in Face Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415560029 ◽

2015 ◽

Vol 29 (03) ◽

pp. 1556002 ◽

Cited By ~ 7

Author(s):

Juan Luis Fernández-Martínez ◽

Ana Cernea

Keyword(s):

Face Recognition ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Majority Voting ◽

Borda Count ◽

Discrete Wavelet ◽

Final Decision ◽

Ensemble Classifiers ◽

Classification Problems ◽

Uncertainty Space

In this paper, we present a supervised ensemble learning algorithm, called SCAV1, and its application to face recognition. This algorithm exploits the uncertainty space of the ensemble classifiers. Its design includes six different nearest-neighbor (NN) classifiers that are based on different and diverse image attributes: histogram, variogram, texture analysis, edges, bidimensional discrete wavelet transform and Zernike moments. In this approach each attribute, together with its corresponding type of the analysis (local or global), and the distance criterion (p-norm) induces a different individual NN classifier. The ensemble classifier SCAV1 depends on a set of parameters: the number of candidate images used by each individual method to perform the final classification and the individual weights given to each individual classifier. SCAV1 parameters are optimized/sampled using a supervised approach via the regressive particle swarm optimization algorithm (RR-PSO). The final classifier exploits the uncertainty space of SCAV1 and uses majority voting (Borda Count) as a final decision rule. We show the application of this algorithm to the ORL and PUT image databases, obtaining very high and stable accuracies (100% median accuracy and almost null interquartile range). In conclusion, exploring the uncertainty space of ensemble classifiers provides optimum results and seems to be the appropriate strategy to adopt for face recognition and other classification problems.

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Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

Academic Journal of Nawroz University ◽

10.25007/ajnu.v7n4a266 ◽

2018 ◽

Vol 7 (4) ◽

pp. 9 ◽

Cited By ~ 1

Author(s):

Shakir F. Kak ◽

Firas M. Mustafa ◽

Pedro R. Valente

Keyword(s):

Face Recognition ◽

Recognition Rate ◽

Training Image ◽

Distance Measures ◽

Manhattan Distance ◽

Face Identification ◽

Discrete Wavelet ◽

Training Images ◽

Matching Process ◽

Cosine Distance

In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.

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Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.121-122.391 ◽

2010 ◽

Vol 121-122 ◽

pp. 391-398 ◽

Cited By ~ 2

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.

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Research of Face Recognition Methods Based on Binding Feature Extraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.668 ◽

2014 ◽

Vol 568-570 ◽

pp. 668-671

Author(s):

Yi Long ◽

Fu Rong Liu ◽

Guo Qing Qiu

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Extraction Method ◽

Local Binary Pattern ◽

Feature Vector ◽

Recognition Rate ◽

Principal Component ◽

Face Image ◽

Feature Extraction Method ◽

Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

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