scholarly journals Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies

2020 ◽  
Vol 37 (6) ◽  
pp. 889-897
Author(s):  
Abderrahmane Herbadji ◽  
Noubeil Guermat ◽  
Lahcene Ziet ◽  
Zahid Akhtar ◽  
Mohamed Cheniti ◽  
...  
Keyword(s):  
Image Features ◽  
Human Hand ◽  
Biometric System ◽  
Person Identification ◽  
Score Level Fusion ◽  
Triangular Norms ◽  
Person Recognition ◽  
Local Ternary Patterns ◽  
Hand Biometrics ◽  
Fingerprint Database

Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to higher person recognition accuracy; besides maintaining hygienic and pandemic concerns. Towards this aim, in this paper, an effective multi-biometric scheme for person authentication based on contactless fingerprint and palmprint selfies has been proposed. In this study, for simplicity and efficiency, three local descriptors, i.e., local phase quantization (LPQ), local Ternary patterns (LTP), and binarized statistical image features (BSIF), have been employed to extract salient features from contactless fingerprint and palmprint selfies. The score level fusion based multi-biometric system developed in this work combines the matching scores using two different fusion techniques, i.e., transformation based-rules like triangular norms and classifier based-rules like SVM. Experimental results on two publicly available databases (i.e., PolyU contactless to contact-based fingerprint database and IIT-Delhi touchless palmprint dataset) show that the proposed contactless multi-biometric selfie system can easily outperform uni-biometrics.

scholarly journals Person Verification Based on Multimodal Biometric Recognition

2021 ◽  
Vol 30 (1) ◽  
pp. 161-183
Author(s):  
Annie Anak Joseph ◽  
Alex Ng Ho Lian ◽  
Kuryati Kipli ◽  
Kho Lee Chin ◽  
Dayang Azra Awang Mat ◽  
...  
Keyword(s):  
Promising Result ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Biometric System ◽  
Score Level Fusion ◽  
Person Recognition ◽  
Biometric Systems ◽  
Verification Process ◽  
Multimodal Biometric Recognition ◽  
Person Verification

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system.

scholarly journals MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yanping Zhang ◽  
Jing Peng ◽  
Xiaohui Yuan ◽  
Lisi Zhang ◽  
Dongzi Zhu ◽  
...  
Keyword(s):  
Prunus Avium ◽  
Cultivar Identification ◽  
Leaf Shape ◽  
Persistent Homology ◽  
Growth Period ◽  
Image Features ◽  
Crop Species ◽  
Score Level Fusion ◽  
Level Fusion ◽  
Leaf Image

AbstractRecognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online.

Multimodal of Ear and Face Biometric Recognition Using AARK Threshold segmentation and Classifier with Score level fusion

2021 ◽  
Author(s):  
SANTHAM BHARATHY ALAGARSAMY ◽  
Kalpana Murugan
Keyword(s):  
Projection Data ◽  
Biometric System ◽  
Biometric Recognition ◽  
Data Set ◽  
Score Level Fusion ◽  
Multimodal Biometric System ◽  
The Individual ◽  
Individual Scores ◽  
Face Biometrics ◽  
Level Fusion

Abstract More than one biometric methodology of an individual is utilized by a multimodal biometric system to moderate a portion of the impediments of a unimodal biometric system and upgrade its precision, security, and so forth. In this paper, an incorporated multimodal biometric system has proposed for the identification of people utilizing ear and face as input and pre-preparing, ring projection, data standardization, AARK limit division, extraction of DWT highlights and classifiers are utilized. Afterward, singular matches gathered from the different modalities produce the individual scores. The proposed framework indicated got brings about the investigations than singular ear and face biometrics tried. To certify the individual as genuine or an impostor, the eventual outcomes are then utilized. On the IIT Delhi ear information base and ORL face data set, the proposed framework has checked and indicated an individual exactness of 96.24%

scholarly journals Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning

2020 ◽  
Vol 10 (2) ◽  
pp. 18-42
Author(s):  
Norah Abdullah Al-johani ◽  
Lamiaa A. Elrefaei
Keyword(s):  
Deep Learning ◽  
Support Vector ◽  
Biometric System ◽  
Score Level Fusion ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Recognition Systems ◽  
Dorsal Hand Vein ◽  
Level Fusion ◽  
Very High

Advancements in biometrics have attained relatively high recognition rates. However, the need for a biometric system that is reliable, robust, and convenient remains. Systems that use palmprints (PP) for verification have a number of benefits including stable line features, reduced distortion and simple self-positioning. Dorsal hand veins (DHVs) are distinctive for every person, such that even identical twins have different DHVs. DHVs appear to maintain stability over time. In the past, different features algorithms were used to implement palmprint (PP) and dorsal hand vein (DHV) systems. Previous systems relied on handcrafted algorithms. The advancements of deep learning (DL) in the features learned by the convolutional neural network (CNN) has led to its application in PP and DHV recognition systems. In this article, a multimodal biometric system based on PP and DHV using (VGG16, VGG19 and AlexNet) CNN models is proposed. The proposed system is uses two approaches: feature level fusion (FLF) and Score level fusion (SLF). In the first approach, the features from PP and DHV are extracted with CNN models. These extracted features are then fused using serial or parallel fusion and used to train error-correcting output codes (ECOC) with a support vector machine (SVM) for classification. In the second approach, the fusion at score level is done with sum, max, and product methods by applying two strategies: Transfer learning that uses CNN models for features extraction and classification for PP and DHV, then score level fusion. For the second strategy, features are extracted with CNN models for PP and DHV and used to train ECOC with SVM for classification, then score level fusion. The system was tested using two DHV databases and one PP database. The multimodal system is tested two times by repeating PP database for each DHV database. The system achieved very high accuracy rate.

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