(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 1, 2021 An Improved Biometric Fusion System of Fingerprint and Face using Whale Optimization Tajinder Kumar 1 Department of Computer Science & Engineering IKG Punjab Technical University Kapurthala, Punjab, India Shashi Bhushan 2 Department of Computer Science and Engineering, Chandigarh Group of College, Landran, Punjab, India Surender Jangra 3 Department of Computer Science, GTBC, Bhawanigarh, Punjab, India Abstract—In the field of wireless multimedia authentication unimodal biometric model is commonly used but it suffers from spoofing and limited accuracy. The present work pro- poses the fusion of features of face and fingerprint recognition system as an Improved Biometric Fusion System (IBFS) leads to improvement in performance. Integrating multiple biometric traits recognition performance is improved and thereby reducing fraudulent access.The paper introduces an IBFS comprising of authentication systems that are Improved Fingerprint Recogni- tion System (IFPRS) and Improved Face Recognition System (IFRS) are introduced. Whale optimization algorithm is used with minutiae feature for IFPRS and Maximally Stable External Regions (MSER) for IFRS. To train the designed IBFS, Pattern net model is used as a classification algorithm. Pattern net works based on processed data set along with SVM to train the IBFS model to achieve better classification accuracy. It is observed that the proposed fusion system exhibited average true positive rate and accuracy of 99.8 percentage and 99.6 percentage, respectively. Keywords—Biometric fusion; face recognition; fingerprint recognition; feature extraction; feature optimization; classifier I. I NTRODUCTION The unimodal biometric systems are prone to the incon- sistencies scores and outliers [1]. However, fusion biometric systems or multi model systems play a crucial role in providing accuracy, security, universality and cost-effectiveness. Various research papers has focused on the face and fingerprint- based fusion system, but classification accuracy has not been achieved so far. This work aims to present a new biometric sys- tem that overcomes the problem of authenticity and employs fusion of faces and fingerprint traits [2].The paper organizes in following sections: Section 2 defines the existing work in fingerprint and face biometric traits in the form of related work . Section 3 presents the formulation of multimodal fusion system. In Section 4, experimental results are considered, and Section 5 states the vital conclusions. II. RELATED WORK Nagar et al. had studied the fusion of three biological features (fingerprint, face and iris). It has been proved that multi-biometric systems show a great similarity check and possess high security although it requires a large storage space to save multiple templates of the input data. Here fuzzy vault and fuzzy commitment model was used to form a biometric encryption system framework [3]. A lot of work has been implemented in the fields of biological key using uni- model biometric system in which algorithm utilized user-key and biometrics authentication [4]. The Dialog Communication Systems developed BioID which is a multimodal identification system that uses three different features: voice, face and lip movement-to recognize people [5]. The present work involves the design of new soft biometric database with a human face, body with clothing attributes at three different distances for investigates indirect influence on the soft biometric fusion [6].Thereafter authors has been designed multimodal biometric approach and validated on three chimeric multimodal databases such as face, fingerprint and Iris. The performance of this work has been analyzed in terms of Decidability Index (DI), Equal Error Rate (EER), and Recognition Index (RI). Present approach outperforms existing methods by achieved an accu- racy of 99.5%, an EER of 0.5% [7]. A novel multi-modal biometric system based on face and fingerprint resolves the issues like noise sensor data, non-universality, susceptibility to forgery and deficiency of invariant representation. The fingerprint matching has been performed through alignment- based elastic algorithm. To get the improved feature extraction, extended local binary patterns (ELBP) has been utilized. This work has been examined on FVC 2000 DB1, FVC 2000 DB2, ORL (AT&T) and YALE databases and accomplished 99.59% of accuracy [8]. The authors have introduced and examined the concepts of dual authority with authentication-based scheme on the basis of biometric along with its influencing factors of recognition rate in biometrics. However the singular model authentication approach does not guarantee the security of jurisdictions in a common regulatory area. By increasing the number of experiments, the average time for face recognizing reduces and leading to the stable value and the rate of recognition to be 92.2%. The authors has explored the ways to enhance the performance of rank identification in Automated Fingerprint Identification Systems (AFIS) while only a partial latent fingerprint has been made available. The preset work approach exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This novel ap- proach has combined with existing minuate-based matcher. A realistic forensic fingerprint casework database comprising of rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency has been utilized here [9]. The authors have presented a novel multimodal biometric fusion system using three different biometric modalities such as face, ear and gait based on speed-up-robust-feature (SURF) descriptor including genetic algorithm (GA). ANN has been utilized to www.ijacsa.thesai.org 664 | Page