scholarly journals A Robust Texture Based Ear and Palmprint Recognition Using Histogram of Oriented Gradients

2019 ◽  
Vol 8 (6) ◽  
pp. 1964-1971
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Palmprint Recognition ◽  
Histogram Of Oriented Gradients ◽  
Biometric Recognition ◽  
Local Descriptor ◽  
Depth Data ◽  
Palm Print ◽  
Level Fusion

Recent research in the surface-based ear and palm print recognition additionally shows that ear identification and palm print identification. The surface-based ear and palm print recognition are strong against sign corruption and encoding antiques. Based on these discoveries, further research and look at the comparison of surface descriptors for ear and palm print recognition and try to investigate potential outcomes to supplement surface descriptors with depth data. The proposed Multimodal ear and palm print Biometric Recognition work is based on the feature level fusion. Based on the ear images and palm print images from noticeable brightness as well as profundity records, we remove surface with outside labels starting complete contour images. In this paper, think about the recognition performance of choose strategies for describing the surface structure, which is Local Binary Pattern (LBP), Weber Local Descriptor (WLD), Histogram of oriented gradients (HOG), and Binarised Statistical Image Features (BSIF). The broad test examination dependent scheduled target IIT Delhi-2 ear and IIT Delhi palm print records affirmed to facilitate and expected multimodal biometric framework can build recognition rates contrasted and that delivered by single-modular for example, Unimodal biometrics. The proposed method Histogram of Oriented Gradients (HOG) achieving a recognition rate of 124%

Online Biological Literature MRI Image Classification Study

2013 ◽  
Vol 694-697 ◽  
pp. 2336-2340
Author(s):  
Yun Feng Yang ◽  
Feng Xian Tang
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Feature Identification ◽  
Identification Method ◽  
Image Library ◽  
Text Information ◽  
Correct Recognition Rate ◽  
Biological Literature ◽  
Mri Image

In order to construct a certain standard structure MRI (Magnetic resonance imaging) image library by extracting and collating unstructured literature data information, an identification method of the image and text information fusion is proposed. The method makes use of PHOW (Pyramid Histogram Of Words) to represent image features, combines with the word frequency characteristics of the embedded icon note (text), and then uses posterior multiplication fusion method to complete the classification and identification of the online biological literature MRI image. The experimental results show that this method has better correct recognition rate and better recognition performance than feature identification method only based on PHOW or text. The study can offer use for reference to construct other structured professional database from online literature.

scholarly journals Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5523 ◽  
Author(s):  
Nada Alay ◽  
Heyam H. Al-Baity
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Identification System ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Softmax Classifier ◽  
Finger Vein ◽  
Level Fusion

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

Feature fusion of palmprint and face via tensor analysis and curvelet transform

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
X. Xu ◽  
X. Guan ◽  
D. Zhang ◽  
X. Zhang ◽  
W. Deng ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Curvelet Transform ◽  
Image Features ◽  
Tensor Analysis ◽  
Small Samples ◽  
Palmprint Recognition ◽  
Biometric Recognition ◽  
Fusion Level ◽  
Knn Classifier ◽  
Multimodal Biometric Recognition

AbstractIn order to improve the recognition accuracy of the unimodal biometric system and to address the problem of the small samples recognition, a multimodal biometric recognition approach based on feature fusion level and curve tensor is proposed in this paper. The curve tensor approach is an extension of the tensor analysis method based on curvelet coefficients space. We use two kinds of biometrics: palmprint recognition and face recognition. All image features are extracted by using the curve tensor algorithm and then the normalized features are combined at the feature fusion level by using several fusion strategies. The k-nearest neighbour (KNN) classifier is used to determine the final biometric classification. The experimental results demonstrate that the proposed approach outperforms the unimodal solution and the proposed nearly Gaussian fusion (NGF) strategy has a better performance than other fusion rules.

scholarly journals A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ujwalla Gawande ◽  
Mukesh Zaveri ◽  
Avichal Kapur
Keyword(s):  
Learning Algorithm ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Rejection Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Fusion Algorithm ◽  
Biometric Systems ◽  
Feature Level Fusion ◽  
Level Fusion

Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.

Magnitude and Phase of Discriminative Orthogonal Radial Moments for Face Recognition

2014 ◽  
pp. 131-160
Author(s):  
Neerja Mittal ◽  
Ekta Walia ◽  
Chandan Singh
Keyword(s):  
Computational Complexity ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Careful Selection ◽  
Phase Information ◽  
Additional Time ◽  
Reduced Dimensions ◽  
Radial Moments ◽  
Selection Of

It is well known that the careful selection of a set of features, with higher discrimination competence, may increase recognition performance. In general, the magnitude coefficients of some selected orders of ZMs and PZMs have been used as invariant image features. The authors have used a statistical method to estimate the discrimination strength of all the coefficients of ZMs and PZMs. For classification, only the coefficients with estimated higher discrimination strength are selected and are used in the feature vector. The performance of these selected Discriminative ZMs (DZMs) and Discriminative PZMs (DPZMs) features are compared to that of their corresponding conventional approaches on YALE, ORL, and FERET databases against illumination, expression, scale, and pose variations. In this chapter, an extension to these DZMs and DPZMs is presented by exploring the use of phase information along with the magnitude coefficients of these approaches. As the phase coefficients are computed in parallel to the magnitude, no additional time is spent on their computation. Further, DZMs and DPZMs are also combined with PCA and FLD. It is observed from the exhaustive experimentation that with the inclusion of phase features the recognition rate is improved by 2-8%, at reduced dimensions and with less computational complexity, than that of using the successive ZMs and PZMs features.

scholarly journals Enhanced Palmprint Identification Using Score Level Fusion

2021 ◽  
pp. 76-81
Author(s):  
Mrs. G. Ananthi ◽  
Dr. J. Raja Sekar ◽  
D. Apsara ◽  
A. K. Gajalakshmi ◽  
S. Tapthi
Keyword(s):  
Recognition Rate ◽  
Score Level Fusion ◽  
Biometric Security ◽  
Palmprint Identification ◽  
Training Samples ◽  
Multiple Feature ◽  
Feature Level Fusion ◽  
Palm Print ◽  
Occurrence Matrix ◽  
Level Fusion

Palm print identification has been used in various applications in several years. Various methods have been proposed for providing biometric security through palm print authentication. One such a method was feature level fusion which used multiple feature extraction and gives higher accuracy. But it needed to design a new matcher and acquired many training samples. However, it cannot adapt to scenarios like multimodal biometric, regional fusion, contactless and complete direction representation. This problem will be overcome by score level fusion method. In this article, we propose a salient and discriminative descriptor learning method (SDDLM) and gray-level co-occurrence matrix (GLCM).The score values of SDDLM and GLCM are integrated using score level fusion to provide enhanced score. Experiments were conducted on IITD palm print V1 database. The combination of SDDLM AND GLCM methods will be useful in achieving higher performance. It provides good recognition rate and reduces computation burden.

scholarly journals Finger Texture Verification Systems Based on multiple spectrum Lighting Sensors with Four Fusion Levels

2019 ◽  
Vol 1 (3) ◽  
pp. 1-16
Author(s):  
Musab T. Al-Kaltakchi ◽  
Raid R. Omar ◽  
Hikmat N. Abdullah ◽  
Tingting Han ◽  
Jonathon A. Chambers
Keyword(s):  
State Of The Art ◽  
Recognition Performance ◽  
Skin Area ◽  
Equal Error Rate ◽  
Score Level Fusion ◽  
Biometric Characteristic ◽  
Palm Print ◽  
Verification Systems ◽  
Finger Skin ◽  
Level Fusion

Finger Texture (FT) is one of the most recent attractive biometric characteristic. Itrefers to a finger skin area which is restricted between the fingerprint and the palm print (just after including the lower knuckle). Different specifications for the FT can be obtained by employing multiple images spectrum of lights. This inspired the insight of applying a combination between the FT features that acquired by utilizing two various spectrum lightings in order to attain high personal recognitions. Four types of fusion will be listed and explained here: Sensor Level Fusion (SLF), Feature Level Fusion (FLF), Score Level Fusion (ScLF) and Decision Level Fusion (DLF). Each fusion method is employed and examined for an FT verification system. From the database of Multiple Spectrum CASIA (MSCASIA), FT images have been collected. Two types of spectrum lights have been exploited (the wavelength of 460 nm, which represents a Blue (BLU) light, and the White (WHT) light). Supporting comparisons were performed, including the state-of-the-art. Best recognition performance were recorded for the FLF based concatenation rule by improving the Equal Error Rate (EER) percentages from 5% for the BLU and 7% for the WHT to 2%.

Learning to Recognize 3D Objects using Sparse Depth and Intensity Information

1997 ◽  
Vol 11 (06) ◽  
pp. 909-931 ◽  
Author(s):  
Brendan MccCane ◽  
Terry Caelli ◽  
Olivier de Vel
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Synthetic Data ◽  
Recognition System ◽  
Rule Generation ◽  
3D Objects ◽  
Depth Data ◽  
Intensity Information ◽  
Intensity Images ◽  
Object Parts

In this paper we further explore the use of machine learning (ML) for the recognition of 3D objects in isolation or embedded in scenes. Of particular interest is the use of a recent ML technique (specifically CRG — Conditional Rule Generation) which generates descriptions of objects in terms of object parts and part-relational attribute bounds. We show how this technique can be combined with intensity-based model and scene–views to locate objects and their pose. The major contributions of this paper are: the extension of the CRG classifier to incorporate fuzzy decisions (FCRG), the application of the FCRG classifier to the problem of learning 3D objects from 2D intensity images, the study of the usefulness of sparse depth data in regards to recognition performance, and the implementation of a complete object recognition system that does not rely on perfect or synthetic data. We report a recognition rate of 80% for unseen single object scenes in a database of 18 non-trivial objects.

scholarly journals Security Authentication for Student Cards’ Biometric Recognition Using Viola-Jones Algorithm

2018 ◽  
Vol 11 (1) ◽  
pp. 241
Author(s):  
S. Ibrahim ◽  
K.R. Jamaluddin ◽  
K.A.F.A. Samah
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Biometric Recognition ◽  
Card Holder ◽  
Detection Rates ◽  
Suitable Candidate ◽  
Unauthorized Access ◽  
Security Authentication ◽  
The University

The unauthorized access to the university entrance could be gained by only flashing a student card. This unsecure situation shows the loophole of security authentication in a university. In order to overcome this, a biometric recognition could be the most suitable candidate as it varies uniquely from one person to another. A study on student cards’ biometric recognition using Viola-Jones algorithm is presented as it is proven as a powerful algorithm in terms of superb detection rates and speed.  It is done by comparing the facial structures and features between the student card’s image and the card holder image, thus determining the similarity. The recognition performance is evaluated based on the percentage of similarity using 100 testing images of 50 students. The observation on results obtained the effectiveness of the Viola-Jones features in student cards’ biometric recognition rate.

Combination of SVM and Score Normalization for Person Identification Based on Audio-Visual Feature Fusion

2011 ◽  
Vol 186 ◽  
pp. 236-240
Author(s):  
Jie Cao ◽  
Di Wu ◽  
Zong Li Liu ◽  
Peng Pan
Keyword(s):  
Speaker Recognition ◽  
Feature Fusion ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Computational Cost ◽  
Noisy Environment ◽  
Face Features ◽  
Feature Level Fusion ◽  
Speech Features ◽  
Level Fusion

Aimed at the problem of low accuracy rate for face recognition and speaker recognition in noisy environment, a multi-biometric model fusing face features and speech features is presented by combining Normalization and SVM theory based on the research of feature level fusion. Face features and speech features are first extracted by pulse coupled neural network and VQ-SVM respectively. Then the distance between tested people and template people is calculated after getting the fused feature on the feature level fusion. In order to reduce the computational cost and improve the recognition performance, matching distance is normalized and finally recognized by SVM. Experiment on the ORL database show that even when the signal to noise ratio is declined, recognition rate of the fused system is clearly higher than the single system under noisy environment and the purpose of identity recognition is achieved.

Sign in / Sign up

Export Citation Format

Share Document