Multimodal Biometric Identification System Research Articles

A Smart Security System Using Multimodal Features from Videos

Multi-modal biometric authentication system uses more than one biometric feature and the use of multi-modal biometrics improves security by making the system invulnerable to spoofing attacks. The proposed system uses face and gait biometrics for authentication and identification. The authentication is done in an unobtrusive manner without the knowledge and co-ordination of the user with the help of surveillance cameras. The videos are captured from two surveillance cameras, placed at fronto-parallel and fronto-normal views are given as input to the system. The gait system uses the video from fronto-parallel view and uses a model free approach to extract a spatio temporal motion summary of the gait cycle. The gait features has been compared by calculating the Euclidean distance between them. The face system uses the video from fronto normal view and uses an appearance based approach to extract features from the face of the user. The face features has been compared by calculating Chi-Square dissimilarity between them. The score level fusion is performed to provide an enhanced security system. A threshold value is kept and it is compared with the scores to authenticate the person. The Minimum Distance Classifier is used to identify the person by fusing the multimodal features.

HYBRID IMPROVED BACTERIAL SWARM OPTIMIZATION ALGORITHM FOR HAND-BASED MULTIMODAL BIOMETRIC AUTHENTICATION SYSTEM

This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively. The results indicated the ability of the proposed HIBS in optimization problem where it optimized relevant weights in an authentication system.

Bring your own hand: how a single sensor is bringing multiple biometrics together

In spite of innumerable benefits of multi-biometry authentication, a couple of imaging sensors are required to capture multi-biometric samples, but that increase the overall cost as well as degree of user cooperation. This work presents a single sensor-based multimodal biometric identification system by fusing major finger knuckle, minor finger knuckle, palm print and handprint features of the human hand for enhancing the security and privacy of any consumer device. A virtual imaging device has been suggested to capture palmer and dorsal view of hand with single-shot multi-trait acquisition mechanism. The hand images captured from digital camera are first preprocessed to get the major knuckle, minor knuckle and palm ROI’s. The finger knuckles and palm print ROI’s are then enhanced and transformed to illumination invariant representation using robust encoding techniques, over which ray tracing features are emphasized predominantly. A non-rigid multi-scale approach, namely deep matching, has been employed to obtain the matching score between the corresponding correlation maps for finger knuckle or palm print recognition. Apart from that, we present a new scheme to extract shape, and geometrical features of handprint and employ metric learning-based L2-norm for feature matching in $$2D^{2}{} \textit{PCA}+2D^{2}{} \textit{LDA}$$ space. Five publicly available databases are used to evaluate the effectiveness of proposed approach. Finally, the score-level weighted sum rule fusion has been adopted to combine matching scores of four traits which show that the proposed method outperforms other unimodal and state-of-the-art multimodal identification methods in terms of EER (0.01%), DI (3.64) and CRR (100%).

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Multimodal Authentication of Ocular Biometric and Finger Vein Verification in Smartphones: A Review

Biometric authentication has demanded a lot of attention from the researchers in the current age, as the field aimsto identify human behavioral charcteristics based on fingerprint, finger vein, ocular, face, palm, etc. So, this field is useful in many applications for offering security and authentication of industry or business. Also, the multimodal biometric system is used to provide a greater security and higher reliability that combines two or more biometric identifiers. Finger vein and ocular-based multimodal biometric authentication system are one of the major techniques which have been considered for efficient identification and verification purpose. This system mainly works in some common stages which include, scanning of finger vein and ocular, pre-processing, feature extraction and matching of finger vein and ocular in a database as well. This paper attempts to review various recent and advanced multimodal finger vein and ocular biometric authentication systems. Finally, possible directions in the multimodal biometric authentication system for the future work are also discussed.

Design and Modeling of Real Time Multimodal Biometric Authentication System

Authentication is a major challenge for accessing different web-based applications, information exchange, communicating people, conducting e-business and performing financial operations, access to system like ATM, Wi-Fi etc. Biometrics based authentication could be used to prevent unauthorized access to these systems. Multi-modal biometric authentication system could be used to further strengthen the security by overcoming the limitations imposed by unimodal biometric authentication system. This paper aimed to design and model the multimodal biometric authentication system for secured access to different web applications through WLAN. The proposed system is designed using UML (Unified Modelling Language) diagrams.

Multimodal biometric identification system based on finger‐veins using hybrid rank–decision‐level fusion technique

The highly random manner in which veins spread along a finger, their immunity to counterfeiting, active liveness, and user friendliness make finger veins the best choice for a biometric identification system (BIS). In this paper, veins of six fingers of two hands of a person are used to develop a secure, reliable, and robust multimodal BIS (MBIS). The main structure of the proposed MBIS is based on the effective combination of rank‐ and decision‐level fusion. In the training step, the power (weight) of each single modality is estimated by extracting the information that lies in the cumulative match characteristic (CMC) curve. The testing step consists of two main parts. In the first part, the region of the finger vein is extracted by using a simple method, and then the binarized statistical image features (BSIFs) algorithm is used to extract feature vectors. In the second part, final decision for the test input probe is made by generating ‘top rank‐decision matrix’, which fuses the information of each biometric identifier in the hybrid rank‐decision level. The obtained results show that proposed method is more reliable and accurate than other fusion techniques at the post‐classification fusion level. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Iris Segmentation using Gradient Magnitude and Fourier Descriptor for Multimodal Biometric Authentication System

Perfectly segmenting the area of the iris is one of the most important steps in iris recognition. There are several problematic areas that affect the accuracy of the iris segmentation step, such as eyelids, eyelashes, glasses, pupil (due to less accurate iris segmentation), motion blur, and lighting and specular reflections. To solve these problems, gradient magnitude and Fourier descriptor are employed to do iris segmentation in the proposed Multimodal Biometric Authentication System (MBAS). This approach showed quite promising results, i.e. an accuracy rate of 97%. The result of the iris recognition system was combined with the result of an open-source fingerprint recognition system to develop a multimodal biometrics authentication system. The results of the fusion between iris and fingerprint authentication were 99% accurate. Data from Multimedia Malaysia University (MMUI) and our own prepared database, the SGU-MB-1 dataset, were used to test the accuracy of the proposed system.

Estimation of Performance in Multimodal Biometric based Authentication System using Various Clustering

Objectives: A swift growth of awareness in biometric based applications can be currently seen in the world. It includes different approaches to solve problems at hand. Methods: In this paper, Rough set with different classifiers are discussed to improve the performance of biometric system based authentication. This paper presents software systems based on rough sets comparing performance evaluation with different classifier techniques, fusion methods, filtering techniques and it provide sophisticated graphical environment. The decision fusion approach of multi modal biometric authentication gives the excellent performance in security system. Findings: A special place among different extensions is taken by the loom that replaces imperceptible relation. In the point of many simplification techniques, variant and extension of rough sets in a consistent appearance of the theory and implementation methodology is in place. This paper is meant to fulfil the specified necessitates and various challenges available in multimodal biometric authentication systems. Applications: This is used in National Secure the borders, Organizational/Enterprise, Identity and access management, Personal, Preventing impersonation (ID theft).

스마트폰 환경의 인증 성능 최적화를 위한 다중 생체인식 융합 기법 연구

In this paper, we have proposed personal multimodal biometric authentication system based on face detection, recognition and speaker verification for smart-phone environment. Proposed system detect the face with Modified Census Transform algorithm then find the eye position in the face by using gabor filter and k-means algorithm. Perform preprocessing on the detected face and eye position, then we recognize with Linear Discriminant Analysis algorithm. Afterward in speaker verification process, we extract the feature from the end point of the speech data and Mel Frequency Cepstral Coefficient. We verified the speaker through Dynamic Time Warping algorithm because the speech feature changes in real-time. The proposed multimodal biometric system is to fuse the face and speech feature (to optimize the internal operation by integer representation) for smart-phone based real-time face detection, recognition and speaker verification. As mentioned the multimodal biometric system could form the reliable system by estimating the reasonable performance.

Police forces across the globe implement biometric tech

Law enforcement agencies around the world are increasingly adopting biometric technologies. The Western Identification Network (WIN), a consortium of western US state and local law enforcement agencies, is to adopt NEC's Integra-ID Multimodal Biometric Identification System (MBIS) solution. Also in the US researchers have developed the FaceSketchID System, which matches police suspect sketches with images held on mugshot databases.

Multimodal Biometric Authentication Process for High Secured Border Control

As peace and harmony is the priority in life. This also lies in the hands of our border security. In recent years due to poor border security, the terrorist gained their access into our country and has cost many damages to life and property. If these scenario continues then the peace in the country will become no more. And therefore a well organized and stringent security has to be made sure. Here this paper comprises of a multi level security system using biometrics, Fingerprint scanning along with Iris reorganization. Therefore this implementation promises (a) Faster immigration (b) Criminals or Terrorist can be easily identified Iris and Fingerprint inputs are given by the citizens who emigrate from one country to other country. In control room identifications takes place by fusing inputs then passes the decision signal automatically. The most common unimodal biometric system, it can be seen in most of the places due to its popularity. Its reliability has decreased because it requires larger memory footprint, higher operational cost and it have slower processing speed. So by introducing Multimodal Biometric Identification System 1–3 this uses Iris and Fingerprint for security purpose4. The major advantage of this multimodal approach is that since both modalities utilized the same matcher module the memory footprint of the system is reduced 11. Integrating multiple modalities in user verification and identification leads to high performance, high reliability and high accuracy. So this technique enhances high security in border control and thus saves lives and property.

Extraction and application of dynamic pupillometry features for biometric authentication

Iris recognition is considered one of the most secure biometric recognition methods. However, even this biometric trait can be frauded in some cases. In other cases, the quality of the captured image can affect the system recognition performance. This paper proposes a multimodal biometric authentication system that combines the use of dynamic features from the Pupillary Light Reflex (PLR) and the static features from the iris pattern for a better performance. A dynamic pupillometer device has been developed and a prototype system for features extraction and classification has been implemented. A pupillometric database has been created using data from 90 volunteers, and tests of the biometric system have lead to experimental of 0% EER.

Design and Implementation of Multi-model Biometric Identification System

Biometric recognition used to make personal identification based on pattern recognition methods. This research use fingerprint and iris recognition systems to build an active serial multimodal biometric identification system. Fingerprint identification algorithm is modified to be modified Delaney triangulation system. In the modified system only the surrounded triangles around each minutia were compared between the input and the stored template. While in the iris recognition system the segmentation method is modified and the recognition is based on the correlation filter. The recognition method is applied only on the lower part of the iris region that is less effected with noise. The proposed multimodal system gave a high accuracy with error rate equals (0. 9%).

Multimodal Biometric Identification System: Fusion of Iris and Fingerprint

This paper proposes a biometric person identification system based on fusion of Iris and Fingerprint images. This paper attempts to improve the performance of iris and fingerprintbased identification system by combining the individual features obtained by implementing Iris and Fingerprint identification system. There exist techniques to provide biometric identification systems based on one or other type of modalities. But usage of multiple modalities has resulted in higher accuracy and reliability as concluded by the experiments conducted.

A Robust Multimodal Biometric System for Person Identification

A more secured multimodal biometric Identification system can be developed using multiple biometric traits. We have used fingerprint and ear as biometric traits to provide a robust Identification System with reduced computational complexity and increased recognition rate. Here in this paper we have proposed and implemented a new enhanced edge intersection point detection method which is used to extract the required ear features .The fingerprint features are extracted using Gabor feature extraction. Extracted features are then fused by concatenation and stored in the database. For recognition, the fused test feature vectors are compared with stored data in the database using Euclidean distance. By this new enhanced method of feature extraction we were able to achieve a matching rate of 100% .Cumulative Match Characteristic (CMC) and Receiver Operating Characteristic (ROC) were plotted and it was observed that this enhanced method provides good results compared to other methods. We have also tested the classification of matching rate using confusion matrix which shows that our method produces perfect classification.

Mobile Multimodal Biometric System for Security

Among our huge life requirements, the most important requirement which has a vital role in our daily life is the security. Recently using biometrics in the security systems has a wide range of research interests since they provide systems have more efficient, reliable, and secure than the others. In the present time researches for mobile biometric devices are provided with a fingerprint only which isn’t sufficient for the areas where lawlessness and anarchy cases are existed recently due to the current political situation in some developing countries in Africa and Middle East where the rate of crime increased significantly involving a large number of people getting into the crime scene. Police departments are unable to identify robbers and criminals due to the presence of newly unrecorded ones with the lack of presence of a fixed database holding records of widely known criminals. So in this paper we propose a mobile biometric authentication system (MOBAS) based on Zigbee technology and a multimodal biometric authentication system in which the primary biometric modalities adopted are the fingerprint and the face recognition. The proposed system aims to provide a mobile, light, user friendly, reliable and secure biometric authentication system to the police departments, and that will help the officers anywhere to identify the criminals by taking a fingerprint or capturing an image or both for different scenarios, sending them wirelessly to the server at the appropriate police station and then waiting for a response concerning the person’s criminal record sent wirelessly from the police station and displayed on a mobile biometric authentication device (MOBA). According to the sent criminal record, the police officers will take a suitable action towards this person.

Biometric authentication based on PCG and ECG signals: present status and future directions

Due to the great advances in biomedical digital signal processing, new biometric traits have showed noticeable improvements in authentication systems. Recently, the ElectroCardioGram (ECG) and the PhonoCardioGraph (PCG) have been proposed as novel biometrics. This paper aims to review the previous studies related to the usage of the ECG and PCG signals in human recognition. In addition, we discuss briefly the most important techniques and methodologies used by researchers in the preprocessing, feature extraction and classification of the ECG and PCG signals. At the end, we introduce some future considerations that can be applied in this topic such as: the fusion between different techniques previously used, use both ECG and PCG signals in a multimodal biometric authentication system and building a prototype system for real-time authentication.

Adaptive Cascade Classifier based Multimodal Biometric Recognition and Identification System

Biometrics consists of techniques for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits such as Iris, fingerprint, Face and Palm geometry etc. To overcome the limitations of Unimodal biometric system, a multimodal biometric is proposed. Amongst the various fusion levels, feature level fusion is expected to offer better recognition. Feature level fusion fused the extracted feature obtained from biometric traits. The proposed system is based on feature level fusion and adaptive cascade classifier for precise and reliable multimodal recognition and identification. Verification of Genuine and imposter individual classification is done using Backpropagation neural network. The simulation results demonstrated that a multibiometric template provides better recognition performance compared to a unibiometric template and adaptive cascade classification system significantly outperforms single classifier.

Development of a Multimodal Biometric Identification and Verification System using Two Fingerprints

Fingerprint recognition is one of the research hotspots of biometrics techniques. Fingerprints are the most widely used biometric feature for identification and verification in the field of biometrics. [1]. The traditional fingerprint recognition systems have such disadvantages as high computation complexity, low speed, low recognition rate to uncompleted or defiled fingerprints, and not robust[2]. In this paper, we propose a multimodel fingerprint identification and verification method based on pattern recognition, which emphasizes global features of fingerprint. With lots of artificial fingerprint samples, the results show that the proposed method is effective, fast, robust and shows the Improvement in rate of false acceptance and false rejections. Experimental results are analyzed and a fingerprint recognition system is introduced. General Terms Pattern Recognition, Fingerprint classification and verification