Person Verification Based on Multimodal Biometric Recognition

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.

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

The electrocardiogram (ECG) based biometric sys-<br>tem has recently gained popularity. Easy signal acquisition and<br>robustness against falsification are the major advantages of the<br>ECG based biometric system. This biometric system can help<br>automate the subject identification and authentication aspect of<br>personalised healthcare services. In this paper, we have designed<br>a novel attention based hierarchical long short-term memory<br>(LSTM) model to learn the biometric representation correspond-<br>ing to a person. The hierarchical LSTM model proposed in this<br>paper can learn the temporal variation of the ECG signal in<br>different abstractions. This addresses the long term dependency<br>issue of the LSTM network in our application. The attention<br>mechanism of the model learns to capture the ECG complexes<br>that have more biometric information corresponding to each<br>person. These ECG complexes are given more weight to learn<br>better biometric representation. The proposed system is less<br>complex and more efficient as it does not require the detection<br>of any fiducial points. We have evaluated the proposed model for<br>both the person verification and identification problems using<br>two on-the-person ECG databases and two off-the-person ECG<br>databases. The proposed framework is found to perform better<br>than the existing fiducial and non-fiducial point based methods.<br>

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

The electrocardiogram (ECG) based biometric sys-<br>tem has recently gained popularity. Easy signal acquisition and<br>robustness against falsification are the major advantages of the<br>ECG based biometric system. This biometric system can help<br>automate the subject identification and authentication aspect of<br>personalised healthcare services. In this paper, we have designed<br>a novel attention based hierarchical long short-term memory<br>(LSTM) model to learn the biometric representation correspond-<br>ing to a person. The hierarchical LSTM model proposed in this<br>paper can learn the temporal variation of the ECG signal in<br>different abstractions. This addresses the long term dependency<br>issue of the LSTM network in our application. The attention<br>mechanism of the model learns to capture the ECG complexes<br>that have more biometric information corresponding to each<br>person. These ECG complexes are given more weight to learn<br>better biometric representation. The proposed system is less<br>complex and more efficient as it does not require the detection<br>of any fiducial points. We have evaluated the proposed model for<br>both the person verification and identification problems using<br>two on-the-person ECG databases and two off-the-person ECG<br>databases. The proposed framework is found to perform better<br>than the existing fiducial and non-fiducial point based methods.<br>

A smartphone ocular alignment measurement app in school screening for strabismus

Background Strabismus is the leading risk factor for amblyopia, which should be early detected for minimized visual impairment. However, traditional school screening for strabismus can be challenged due to several factors, most notably training, mobility and cost. The purpose of our study is to evaluate the feasibility of using a smartphone application in school vision screening for detection of strabismus. Methods The beta smartphone application, EyeTurn, can measure ocular misalignment by computerized Hirschberg test. The application was used by a school nurse in a routine vision screening for 133 elementary school children. All app measurements were reviewed by an ophthalmologist to assess the rate of successful measurement and were flagged for in-person verification with prism alternating cover test (PACT) using a 2.4Δ threshold (root mean squared error of the app). A receiver operating characteristic (ROC) curve was used to determine the best sensitivity and specificity for an 8Δ threshold (recommended by AAPOS) with the PACT measurement as ground truth. Results The nurse obtained at least one successful app measurement for 93% of children (125/133). 40 were flagged for PACT, of which 6 were confirmed to have strabismus, including 4 exotropia (10△, 10△, 14△ and 18△), 1 constant esotropia (25△) and 1 accommodative esotropia (14△). Based on the ROC curve, the optimum threshold for the app to detect strabismus was determined to be 3.0△, with the best sensitivity (83.0%), specificity (76.5%). With this threshold the app would have missed one child with accommodative esotriopia, whereas conventional screening missed 3 cases of intermittent extropia. Conclusions Results support feasibility of use of the app by personnel without professional training in routine school screenings to improve detection of strabismus.