This paper presents a novel method of a secured card-less AutomatedTeller Machine (ATM) authentication based on the three bio-metrics measures. Itwould help in the identification and authorization of individuals and w...This paper presents a novel method of a secured card-less AutomatedTeller Machine (ATM) authentication based on the three bio-metrics measures. Itwould help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identi-fication in ways that cannot be impersonated. To the best of our knowledge, thismethod of Biometric_ fusion way is the first ATM security algorithm that utilizesa fusion of three biometric features of an individual such as Fingerprint, Face, andRetina simultaneously for recognition and authentication. These biometric imageshave been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting theseimages to YIQ color space, which is helpful in normalizing the brightness levelsof the image hence mainly (Y component’s) luminance. Then, it attempt toenhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these criticalsegments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion ofextracted features of all three biometrics features have been used to bring in themultimodal classification approach to get fusion vectors. Once fusion vectorsware formulated, the feature level fusion technique is incorporated based on theextracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATMsecurity. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has beenselected based on the effectiveness of the features. The proposed approach resultswere compared with existing algorithms based on the classification accuracy toprove the展开更多
基金The authors may receive funding for this study after acceptance from Majma'ah University research deanship.
文摘This paper presents a novel method of a secured card-less AutomatedTeller Machine (ATM) authentication based on the three bio-metrics measures. Itwould help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identi-fication in ways that cannot be impersonated. To the best of our knowledge, thismethod of Biometric_ fusion way is the first ATM security algorithm that utilizesa fusion of three biometric features of an individual such as Fingerprint, Face, andRetina simultaneously for recognition and authentication. These biometric imageshave been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting theseimages to YIQ color space, which is helpful in normalizing the brightness levelsof the image hence mainly (Y component’s) luminance. Then, it attempt toenhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these criticalsegments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion ofextracted features of all three biometrics features have been used to bring in themultimodal classification approach to get fusion vectors. Once fusion vectorsware formulated, the feature level fusion technique is incorporated based on theextracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATMsecurity. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has beenselected based on the effectiveness of the features. The proposed approach resultswere compared with existing algorithms based on the classification accuracy toprove the
