Verisoft – secure biometric identification system

2008 ◽  
pp. 83-97
Author(s):  
Georg Rock ◽  
Gunter Lassmann ◽  
Mathias Schwan ◽  
Lassaad Cheikhrouhou
Keyword(s):  
Biometric Identification ◽  
Identification System

Biometric Identification System Using Neuro and Fuzzy Computational Approaches

2016 ◽  
pp. 335-366
Author(s):  
Tripti Rani Borah ◽  
Kandarpa Kumar Sarma ◽  
Pranhari Talukdar
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Biometric Identification ◽  
Identification System ◽  
Inference System ◽  
Computational Approaches ◽  
Neuro Fuzzy ◽  
Actual Design ◽  
Authentication System ◽  
Numeric Data

In all authentication systems, biometric samples are regarded to be the most reliable one. Biometric samples like fingerprint, retina etc. is unique. Most commonly available biometric system prefers these samples as reliable inputs. In a biometric authentication system, the design of decision support system is critical and it determines success or failure. Here, we propose such a system based on neuro and fuzzy system. Neuro systems formulated using Artificial Neural Network learn from numeric data while fuzzy based approaches can track finite variations in the environment. Thus NFS systems formed using ANN and fuzzy system demonstrate adaptive, numeric and qualitative processing based learning. These attributes have motivated the formulation of an adaptive neuro fuzzy inference system which is used as a DSS of a biometric authenticable system. The experimental results show that the system is reliable and can be considered to be a part of an actual design.

An accurate multi-modal biometric identification system for person identification via fusion of face and finger print

2019 ◽  
Vol 23 (2) ◽  
pp. 1299-1317 ◽  
Author(s):  
Sidra Aleem ◽  
Po Yang ◽  
Saleha Masood ◽  
Ping Li ◽  
Bin Sheng
Keyword(s):  
Biometric Identification ◽  
Identification System ◽  
Person Identification ◽  
Finger Print

HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition

Sensor Review ◽  
2020 ◽  
Vol 40 (2) ◽  
pp. 203-216
Author(s):  
S. Veluchamy ◽  
L.R. Karlmarx
Keyword(s):  
Research Field ◽  
Biometric Identification ◽  
Svm Classifier ◽  
Identification System ◽  
Biometric Recognition ◽  
Data Set ◽  
Content Type ◽  
Proposed Model ◽  
Simulation Results ◽  
Multimodal Biometric Recognition

Purpose Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features. Design/methodology/approach The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector. Findings Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1. Originality/value In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

The Image Process Technology in Fingerprint Identification

2012 ◽  
Vol 433-440 ◽  
pp. 3247-3251
Author(s):  
Hai Yan Chen ◽  
Ling Hui
Keyword(s):  
Social Life ◽  
Fingerprint Identification ◽  
Biometric Identification ◽  
Identification System ◽  
Image Process ◽  
Process Technology ◽  
Fingerprint Image ◽  
Image Preprocessing

Fingerprint identification technology is one of the biometric identification technologies that match and recognize the collected fingerprint image to determine the identity of the person. Fingerprint identification technology compared to other biometric identifications is more unique, practical and workable. Therefore, the fingerprint identification as the most popular, most convenient and most reliable authentication method, it has been widely used in many aspects of social life. This paper introduced the whole structure of the fingerprint identification system and the function of every part in fingerprint identification system, mainly introduced the part of fingerprint image pre-processing and implemented one of the algorithms for fingerprint image preprocessing with MATLAB. The result of the experiment shows that the algorithm for fingerprint image preprocessing that used in the paper meets the fingerprint image preprocessing requirements and it provides a basis for fingerprint characteristics extracting and fingerprint characteristics matching.

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