Person Identification Using Multimodal Biometrics under Different Challenges

The main aims of this chapter are to show the importance and role of human identification and recognition in the field of human-robot interaction, discuss the methods of person identification systems, namely traditional and biometrics systems, and compare the most commonly used biometric traits that are used in recognition systems such as face, ear, palmprint, iris, and speech. Then, by showing and comparing the requirements, advantages, disadvantages, recognition algorithms, challenges, and experimental results for each trait, the most suitable and efficient biometric trait for human-robot interaction will be discussed. The cases of human-robot interaction that require to use the unimodal biometric system and why the multimodal biometric system is also required will be discussed. Finally, two fusion methods for the multimodal biometric system will be presented and compared

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithm

UBSegNet: Unified Biometric Region of Interest Segmentation Network

Digital human identity management, can now be seen as a social necessity, as it is essentially required in almost every public sector such as, financial inclusions, security, banking, social networking e.t.c. Hence, in today's rampantly emerging world with so many adversarial entities, relying on a single biometric trait is being too optimistic. In this paper, we have proposed a novel end-to-end, Unified Biometric ROI Segmentation Network (UBSegNet), for extracting region of interest from five different biometric traits viz. face, iris, palm, knuckle and 4-slap fingerprint. The architecture of the proposed UBSegNet consists of two stages: (i) Trait classification and (ii) Trait localization. For these stages, we have used a state of the art region based convolutional neural network (RCNN), comprising of three major parts namely convolutional layers, region proposal network (RPN) along with classification and regression heads. The model has been evaluated over various huge publicly available biometric databases. To the best of our knowledge this is the first unified architecture proposed, segmenting multiple biometric traits. It has been tested over around 5000 * 5 = 25,000 images (5000 images per trait) and produces very good results. Our work on unified biometric segmentation, opens up the vast opportunities in the field of multiple biometric traits based authentication systems.Comment: 4th Asian Conference on Pattern Recognition (ACPR 2017

Integrated Biometric Template Security using Random Rectangular Hashing

Large centralized biometric databases, accessible over networks in real time are especially used for identification purposes. Multimodal biometric systems which are more robust and accurate in human identification require multiple templates storage of the same user analogous to individual biometric sources. This may raises concern about their usage and security when these stored templates are compromised since each person is believed to have a unique biometric trait. Unlike passwords, the biometric templates cannot be revoked and switch to another set of uncompromised identifiers when compromised. Therefore, fool-proof techniques satisfying the requirements of diversity, revocability, security and performance are required to protect stored templates such that both the security of the application and the users2019; privacy are not compromised by the impostor attacks. Thus, this paper proposes a template protection scheme coined as random rectangular hashing to strengthen the multimodal biometric system. The performance of the proposed template protection scheme is measured using the fingerprint FVC2004 and PolyU palmprint database

Human Identification Based on Electrocardiogram and Palmprint

In this paper, a new approach in human identification is investigated. For this purpose, we fused ECG and Palm print biometrics to achieve a multimodal biometric system. In the proposed system for fusing biometrics, we used MFCC approach in order to extract features of ECG biometric and PCA to extract features of Palm print. The features undergo a KNN classification. The performance of the algorithm is evaluated against the standard MIT-BIH and POLYU databases. Moreover, in order to achieve more realistic and reliable results, we gathered Holter ECG recordings acquired from 50 male and female subjects in age between 18 and 54. The numerical results indicated that the algorithm achieved 94.7% of the detection rate.DOI:http://dx.doi.org/10.11591/ijece.v2i2.29

IDENTITY RECOGNITION OPTIMIZATION BASED ON LBP FEATURE EXTRACTION

ABSTRACT Unimodal systems have limited information that can be used for identity recognition systems. The multimodal system was created to improve the unimodal system. The multimodal system used in this study is the combination of the face and palms at the matching score level. Matching scores is done using the Weighted Sum Rule method. Extract features from each sample using the Local Binary Pattern (LBP) method. Meanwhile, large data dimensions are reduced by using the Principal Component Analysis (PCA) method. The distance between face and palm data is measured using the closest distance, namely the Euclidean Distance method. Benchmark dataset using ORL, FERET and PolyU. Based on testing on each database, an accuracy rate of 98% (ORL and PolyU) and 95% (FERET and PolyU) is obtained. The test results show that the multimodal system using the Hybrid method (PCA and LBP) biometric system runs well and optimally. Keywords: Artificial intelegency, recognition, LBP, multimoda

A Longitudinal Analysis on the Feasibility of Iris Recognition Performance for Infants 0-2 Years Old

The focus of this study was to longitudinally evaluate iris recognition for infants between the ages of 0 to 2 years old. Image quality metrics of infant and adult irises acquired on the same iris camera were compared. Matching performance was evaluated for four groups, infants 0 to 6 months, 7 to 12 months, 13 to 24 months, and adults. A mixed linear regression model was used to determine if infants’ genuine similarity scores changed over time. This study found that image quality metrics were different between infants and adults but in the older group, (13 to 24 months old) the image quality metric scores were more likely to be similar to adults. Infants 0 to 6 months old had worse performance at an FMR of 0.01% than infants 7 to 12 months, 13 to 24 months, and adults

Edge-centric multimodal authentication system using encrypted biometric templates

Data security, complete system control, and missed storage and computing opportunities in personal portable devices are some of the major limitations of the centralized cloud environment. Among these limitations, security is a prime concern due to potential unauthorized access to private data. Biometrics, in particular, is considered sensitive data, and its usage is subject to the privacy protection law. To address this issue, a multimodal authentication system using encrypted biometrics for the edge-centric cloud environment is proposed in this study. Personal portable devices are utilized for encrypting biometrics in the proposed system, which optimizes the use of resources and tackles another limitation of the cloud environment. Biometrics is encrypted using a new method. In the proposed system, the edges transmit the encrypted speech and face for processing in the cloud. The cloud then decrypts the biometrics and performs authentication to confirm the identity of an individual. The model for speech authentication is based on two types of features, namely, Mel-frequency cepstral coefficients and perceptual linear prediction coefficients. The model for face authentication is implemented by determining the eigenfaces. The final decision about the identity of a user is based on majority voting. Experimental results show that the new encryption method can reliably hide the identity of an individual and accurately decrypt the biometrics, which is vital for errorless authentication