International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 13 (2018) pp. 11279-11284 © Research India Publications. http://www.ripublication.com 11279 Performance Evaluation of PPG based multimodal biometric system using modified Min-Max Normalization. Girish Rao Salanke N S 1 , Dr. M V Vijaya Kumar 2 , Dr. Andrews Samraj 3 1 Assistant Professor, Department of Computer Science & Engineering, R V College of Engineering, Bangalore, India. 2 Professor, Department of Computer Science & Engineering, Dr Ambedkar Institute of Technology, Bangalore, India. 3 Director, Advance Science and Technology Research Center, Mahendra College of Engineering, Salem, India. Abstract Usage of Photoplethysmograhy (PPG) signal which was limited for clinical purposes is explored for the biometric field by fusing it with a traditional biometric such as fingerprint. A multimodal biometric system is proposed to overcome the limitations of unimodal biometric system. A modified Min- Max Normalization score level fusion is proposed for multimodal biometric system. The paper evaluates the performance of PPG based multimodal biometric approach where in it is observed that the False Acceptance Rate of fingerprint biometric system is reduced from 5.4 % to 3 % and similarly the False Rejection Rate is reduced from 6.7 % to 3.8 % by fusing the PPG component with fingerprint. The proposed method exhibits good identification accuracies when PPG signal is used as one of the biometric trait in a multimodal biometric system. Keyword: Biometric, PPG signal, Score Fusion, False Acceptance Rate (FAR), False Rejection Rate (FRR), Min- Max Normalization INTRODUCTION The traits used in multimodal biometrics[1] have relayed more on traditional biometrics like fusing physiological features such as face with fingerprint, face with palmprint[2], and face with Iris and so on. Most researches in the biometric community have ignored the intrinsic characteristics of the biological signal for their applications. Studies of such signals that can be used for biometrics are very important. Some of the signals that can be considered are ECG, EEG and PPG[3] signals, which exhibit a rich set of features that can be used for identification and verification purpose. The second objective of this work is to propose a new algorithm that is robust to day today as PPG changes due to motion artifact. Since the PPG signal is a time series there is always the question of how long should the PPG is acquired. Considering that enrolment is done only once, subjects will agree to spend some time enrolling themselves into the system however for verification our goal is to minimize the authentication time for the subjects. The final objective of this work is to explore the effectiveness of using the PPG signal in multimodal biometric systems. Since there is no biometric which has 0% False Rejection Rate and all biometrics have their own limitations and disadvantages, multimodal biometric systems uses more than one biometric traits for the sake of improve the performance and making the system robust to spoof attacks. The main challenge is that to choose appropriate biometrics such that the inherent weaknesses can be offset by overall system design. Therefore in order to further improve the PPG biometric system we propose a multimodal biometric system by fusing PPG and fingerprint. The fingerprint matcher offers high accuracy in terms of authentication however suffers from spoof attacks since a fingerprint trace can be easily taken from any surface that a finger has touched. Finally both modalities can be collected conveniently from subject’s fingertips which require less cooperation from subjects unlike other systems. Multimodal biometrics[4] combines information from different sources as compared to unimodal[5] wherein person recognition is based on a single source of biometric information. Some of the system requirements are not meet in Unimodal biometrics; therefore combining multiple biometrics can overcome the limitations of unimodal biometrics and also improve the performance of the overall system. In multimodal biometrics the sources of information can be from multiple sensors, multiple traits, multiple instances or multiple instances. In multiple sensors the different sensors are used for capturing single biometric trait. For example face images of an individual can be captured using two different sensors. In multiple traits, the system information from different biometric traits are combined to authenticate a subject, for example combing face and fingerprint. In multiple Instances, the systems use multiple instances of a single biometric trait, such as the image of the left and right eye of a subject for a retina recognition system. In multiple Sample, the system uses a single sensor is used to capture multiple samples of a single biometric characteristic of a subject, for example frontal, left and right profiles used in face recognition The fusion[6] in multimodal biometrics can be done at different levels, such as a) Feature level fusion b) Decision level c) Score level. In Feature level fusion feature set extracted from multiple data sources are combined to create a new feature set as shown in figure 1. If the features from different biometrics traits are in the same type of measurement than it is recommendable to combine there features vectors into a single new vector.