Bimodal Biometrics for Health Care Infrastructure Security

 Abstract—the movement of health care sector towards Electronic Medical Record has made the securing of patient medical record to become an increasingly important problem in this technological age as the confidential health information needs to be protected from unauthorized people that can view and edit patient Electronic Medical Record (EMR) on Personal Computer (PC). In order to protect the patient's confidential record, this research integrate the use of bimodal (fingerprint and face) authentication for securing patient medical record on PC. The bimodal biometric (fingerprint and face) are often affected by distortions which are caused by environmental noise such as oil, wrinkles, dry skin and dirt. These often affect the biometric system accuracy during authentication stage. In order to protect and improve the accuracy of patient Electronic medical Record (EMR) in health care infrastructure, this study introduced Modified Gabor Filter (MGF), a fast Principal Component Analysis (PCA) algorithm with Support Vector Machine (SVM) to address the issues of fingerprint and face image distortion respectively. From the experiment conducted from 100 patients with 5 fingerprint and face image from each to give 20 test cases. The result shows that the proposed bimodal biometrics approach gives a lower False Rejection Rate (FRR) and False Acceptance Rate (FAR) and this shows better constructive accuracy of our system on patient EMR in health care infrastructure.