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

A Preliminary Review of Behavioural Biometrics for Health Monitoring in the Elderly

This article explores the potential of ICT-based biometrics for monitoring the health status of the elderly people. It departs from specific ageing and biometric traits to then focus on behavioural biometric traits like handwriting, speech and gait to finally explore their practical application in health monitoring of elderly

Review of Multimodal Biometric Identification Using Hand Feature and Face

In the era of Information Technology, openness of the information is a major concern. As the confidentiality and integrity of the information is critically important, it has to be secured from unauthorized access. Security refers to prohibit some unauthorized persons from some important data or from some precious assets. So we need accurateness on automatic personal identification in various applications such as ATM, driving license, passports, citizen's card, cellular telephones, voter's ID card etc. Unimodal system carries some problems such as Noise in sensed data, Intra-class variations, Inter-class similarities, Non-universality and Spoof attacks. The accuracy of system is improved by combining different biometric traits which are called multimodal. This system gives more accuracy as it would be difficult for imposter to spoof multiple biometric traits simultaneously. This paper reviews different methods for fusion of biometric traits

A Comparative Study of Pixel by Pixel and PCA Technique in Serial Multimodal Biometric

In multimodal biometric technique two or more than two biometric traits are used. So in this, we use two biometric traits they are face and fingerprint. With the help of Pixel by Pixel and PCA technique we identified the user or calculate that the person is authorized or not. So in this paper we compared two techniques i.e. Pixel by Pixel and PCA technique

Secret sharing using biometric traits

In biometric based authentication, biometric traits of a person are matched against his/her stored biometric profile and access is granted if there is sufficient match. However, there are other access scenarios, which require participation of multiple previously registered users for a successful authentication or to get an access grant for a certain entity. For instance, there are cryptographic constructs generally known as secret sharing schemes, where a secret is split into shares and distributed amongst participants in such a way that it is reconstructed/ revealed only when the necessary number of share holders come together. The revealed secret can then be used for encryption or authentication (if the revealed key is verified against the previously registered value). In this work we propose a method for the biometric based secret sharing. Instead of splitting a secret amongst participants, as is done in cryptography, a single biometric construct is created using the biometric traits of the participants. During authentication, a valid cryptographic key is released out of the construct when the required number of genuine participants present their biometric traits

Prediction of Carcass Weight from Live Body Weight and Morpho-Biometric Traits of Male Nigerian Indigenous Chickens Using Path Coefficient Analysis

Carcass weight has great economic importance in poultry industry and is associated with other traits. This study investigates correlations among morpho-biometric traits (body length (BL), thigh length (TL), breast girth (BL), shank length (SL) and wing length (WL), livebody weight (LBW) and carcass weight (CW) in male chickens and quantifies the direct and indirect influence of LBW and morpho-biometric traits on CW. The aforementioned traits were measured in 187 male Nigerian indigenous chickens at 20 weeks of age. Correlation and regression coefficients among the traits were obtained to determine the intensity and nature of their association while the path analysis was used to investigate effects of LBW and morpho-biometric traits on CW trait. All analyses were done by SAS 9.1.3 software. The correlation coefficients among morpho-biometric traits, LBW and CW ranged from 0.1953 to 0.9930. The highest correlation was between LBW and CW (0.9930). The results showed a positive and highly significant correlation (P 0.05). The LBW had the highest direct influence on CW followed by BG. Individual pre-selection for these traits could favour an increased CW in the future generations of this chicken type since the LBW and the BG are directly related to CW

Prediction of Carcass Weight from Live Body Weight and Morpho-Biometric Traits of Male Nigerian Indigenous Chickens Using Path Coefficient Analysis

Carcass weight has great economic importance in poultry industry and is associated with other traits. This study investigates correlations among morpho-biometric traits (body length (BL), thigh length (TL), breast girth (BL), shank length (SL) and wing length (WL), livebody weight (LBW) and carcass weight (CW) in male chickens and quantifies the direct and indirect influence of LBW and morpho-biometric traits on CW. The aforementioned traits were measured in 187 male Nigerian indigenous chickens at 20 weeks of age. Correlation and regression coefficients among the traits were obtained to determine the intensity and nature of their association while the path analysis was used to investigate effects of LBW and morpho-biometric traits on CW trait. All analyses were done by SAS 9.1.3 software. The correlation coefficients among morpho-biometric traits, LBW and CW ranged from 0.1953 to 0.9930. The highest correlation was between LBW and CW (0.9930). The results showed a positive and highly significant correlation (P 0.05). The LBW had the highest direct influence on CW followed by BG. Individual pre-selection for these traits could favour an increased CW in the future generations of this chicken type since the LBW and the BG are directly related to CW

Age-Adaptive Multimodal Biometric Authentication System with Blockchain-based Re-Enrollment

In the long run, a significant time gap between enrollment and probe image challenges the model's prediction ability when it has been trained on variant biometric traits. Since variant biometric traits change over time, it is sensible to construct a multimodal biometric authentication system that must include at least one invariant trait, such as the iris. The emergence of Deep learning has enabled developers to build classifiers on synthesized age-progressive images, particularly face images, to search for individuals who have been missing for many years, to avail a comprehensive portrayal of their appearance. However, in sensitive areas such as the military and banks, where security and confidentiality are of utmost importance, models should be built using real samples, and any variations in biometric traits should trigger an alert for the system and notify the subject about re-enrollment. This paper proposes an algorithm for age adaptation of biometric classifiers using multimodal channels which securely update the biometric traits while logging the transactions on the blockchain. It emphasizes confidence-score-based re-enrolment of individual subjects when the authenticator module becomes less effective with a particular subject's probe image. This reduces the time, cost, and memory involved in periodic re-enrolment of all subjects. The classifier deployed on the blockchain invokes appropriate smart contracts and completes this process securely

Design and implementation of a multi-modal biometric system for company access control

This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman\u2019s method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user\u2019s face only or the serial acquisition of both the user\u2019s face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent \u201cresponse behaviors\u201d according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&T Face Database and the UBIRIS database