Exploring biometric identification in FinTech applications based on the modified TAM

In recent years,biometric technologies have been widely embedded in mobile devices;these technologies were originally employed to enhance the security of mobile devices.With the rise of financial technology(FinTech),which uses mobile devices and applications as promotional platforms,biometrics has the important role of strengthening the identification of such applications for security.However,users still have privacy and trust concerns about biometrics.Previous studies have demonstrated that the technology acceptance model(TAM)can rigorously explain and predict user acceptance of new technologies.This study therefore modifies the TAM as a basic research architecture.Based on a literature review,we add two new variables,namely,“perceived privacy”and“perceived trust,”to extend the traditional TAM to examine user acceptance of biometric identification in FinTech applications.First,we apply the analytic hierarchy process(AHP)to evaluate the defined objects and relevant criteria of the research framework.Second,we use the AHP results in the scenario analysis to explore biometric identification methods that correspond to objects and criteria.The results indicate that face and voice recognition are the two most preferred identification methods in FinTech applications.In addition,there are significant changes in the results of the perceived trust and perceived privacy dominant scenarios.

Gram Matrix-Based Convolutional Neural Network for Biometric Identification Using Photoplethysmography Signal

As a kind of physical signals that could be easily acquired in daily life,photoplethysmography(PPG)signal becomes a promising solution to biometric identification for daily access management system(AMS).State-of-the-art PPG-based identification systems are susceptible to the form of motions and physical conditions of the subjects.In this work,to exploit the advantage of deep learning,we developed an improved deep convolutional neural network(CNN)architecture by using the Gram matrix(GM)technique to convert time-serial PPG signals to two-dimensional images with a temporal dependency to improve accuracy under different forms of motions.To ensure a fair evaluation,we have adopted cross-validation method and“training and testing”dataset splitting method on the TROIKA dataset collected in ambulatory conditions.As a result,the proposed GM-CNN method achieved accuracy improvement from 69.5%to 92.4%,which is the best result in terms of multi-class classification compared with state-of-the-art models.Based on average five-fold cross-validation,we achieved an accuracy of 99.2%,improved the accuracy by 3.3%compared with the best existing method for the binary-class.

Using Head Patch Pattern as a Reliable Biometric Character for Noninvasive Individual Recognition of an Endangered Pitviper Protobothrops mangshanensis

Mangshan pitviper, Protobothrops mangshanensis （formerly Zhaoermia mangshanensis） is endemic to China. Unfortunately, due to the decreasing size of its wild populations, this snake has been listed as critically endangered. Re- search carried out on the Mangshan pitviper＇s population ecology and captive reproduction has revealed that the unique head patch patterns of different individuals may potentially be used as a noninvasive recognition biometric character. We collected head patch pattern images of 40 individuals of P. mangshanensis between 1994 and 2011. By comparing each pitviper＇s head patch pattern, we found that the head patch pattern of individual snakes was different and unique. Additionally, we observed and recorded the head patch pattern characters of four adults and five juveniles before and af- ter ecdysis. Our findings confirmed that head patch patterns of Mangshan pitvipers are unique and stable, remaining un- changed after ecdysis. Thus, individuals can be quickly identified by examining the head patch pattern within a specific recognition area on the head. This method may be useful for noninvasive individual recognition in many other species that display color patch pattern variations, especially in studies of endangered species where the use of invasive marking techniques is undesirable.