Authors: Rejuaro O. O. , Adetunji A. B., Adedeji F., Falohun A. S., Iromini N. A., Adebajo O. O.
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Recently, biometric was being integrated with cryptography (crypto-biometric system) to alleviate the limitations of the biometric or cryptography system. However, the main shortcoming of cryptography is poorly-chosen or forgotten password while challenges with biometrics include interclass similarities in the feature sets used to represent traits. In this work, a combination of cryptography and bimodal biometric was developed, an Advanced Encryption Standard based Fast Fourier Transform (AES-FFT) was developed and used as the cryptography technique. Hence, an attempt was made to develop an improved access control system using an enhanced bimodal bio-cryptography. Biometric features was extracted from individual face and iris after application of suitable preprocessing techniques for each modality using Principal Component Analysis (PCA) while cryptography key was generated using fused features from the face and iris by Advanced Encryption System based Fast Fourier Transform (AES-FFT). The two captured biometric data at acquisition module via webcam were subjected to appropriate pre-processing and feature extraction module. The features extracted were fused at feature level using weighted average and optimal features were selected using genetic programming (GP). The classification technique used was Support Vector Machine (SVM). To supplement the enhancement of the system’s integrity, templates and encrypted data were stored in a database. Access to the database was secured with AES-FFT algorithm. Thereafter, an Access Control System was simulated using MATLAB (version R2020b) and evaluated using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (ERR) and Accuracy. The evaluation results showed that the FAR, FRR, ERR and Accuracy of the fused features using AES-FFT were 1.67, 2.22, 2.78 and 97.92% respectively at 0.76 threshold while for AES, the best values obtained for FAR, FRR, ERR and Accuracy were 3.33, 4.44, 7.90 and 95.83% respectively at same threshold. In view of this, an automated bi-modal crypto-biometric system based on fused iris and face (that is, both face and iris), produced a more reliable accurate and secure system on any repository system as a result of its high accuracy. In other words, the developed AES-FFT technique ensured scalable encrypting capacity, good imperceptibility and security performance, and robustness against various attacks with optimal computational efficiency in terms of its accuracy and time.
Access control in computing is motivated by the need to divulge access to information and available computing resources and services to authorized entities only, through authentication, authorization, and access control. A secure transmission of data for access control system become very important in Information and Communication Technology. A third party can trap data or steal important data stored in a computer. To prevent this, it is advocated to encrypt the messages to provide Information Security (Oluwadamilola et al., 2017).
Biometric identification is an emerging technology which gains more attention in recent years. It employs physiological or behavioral characteristics to identify an individual (Richard et al., 2007, Omidiora et al., 2015). The combination of biometric data systems and biometrics recognition/identification technologies create the biometric security systems. The biometric security system is a lock and capture mechanism to control access to specific data. In order to access the biometric security system, individuals will need to provide their unique characteristics or traits which will be matched to a database in the system. If there is a match, the locking system will provide access to the data for the user. The locking and capturing system will activate and record information of users who accessed the data (Falohun et al., 2016).
Cryptography is a method of storing and transmitting data in a particular form so that only those for whom it is intended can read and process it. The integration of biometric with cryptography, deals with either cryptographic key release or cryptographic key generation, and is promising in many aspects. Cryptography is the science of using mathematics to encrypt and decrypt data to keep messages secured by transforming intelligible data form (plaintext) into unintelligible form (ciphertext) (Marwa et al., 2016). Cryptography is also a branch of science that define the art of secret writing, and it is a technique in which secret data is changed by its character in such a way that any intruder cannot identify the message. As biometric is directly linked with the owner, it removes the problem of memorizing the cryptographic key and confirms the non-repudiation of users (Jain et al., 2014). The authentication systems which integrate biometric traits with cryptography are called crypto-biometric systems (Hao et al. 2006).
In this research, a combination of cryptography and bimodal biometric was developed, an Advanced Encryption Standard based Fast Fourier Transform (AES-FFT) was developed and used as the cryptography technique.
II. RELATED WORKS
Jagadeesan et al., (2010) proposed an efficient approach based on multimodal biometrics (iris and fingerprint) for generating a secure cryptographic key, where the security is further enhanced with the difficulty of factoring large numbers. At first, the features, minutiae points and texture properties are extracted from the iris and fingerprint images respectively. Then, the extracted features are fused at the feature level to obtain the multi-biometric template. Finally, a multi-biometric template is used for generating a 256-bit cryptographic key. For experimentation, the study used the fingerprint images obtained from publicly available sources and the fingerprint images from CASIA Iris Database. The experimental results showed that the generated 256-bit cryptographic key is capable of providing better user authentication and better security.
Wang et al., (2011) proposed a novel multimodal biometric system using face-iris fusion feature. Face feature and iris feature are first extracted respectively and fused in feature-level. However, existing feature level schemes such as sum rule and weighted sum rule are inefficient in complicated condition. In this paper, we adopt an efficient feature-level fusion scheme for iris and face in series. The algorithm normalizes the original features of iris and face using z-score model to eliminate the unbalance in the order of magnitude and the distribution between two different kinds of feature vectors, and then connect the normalized feature vectors in serial rule. The proposed algorithm is tested using CASIA iris database and two face databases (ORL database and Yale database). Experimental results show the effectiveness of the proposed algorithm.
Zuva et al., (2014) proposed using both fingerprint and face for authentication in access system. The study integrated fingerprint and face biometric to improve the performance in access control system. This paper considered restoration of distorted and misaligned fingerprints caused by environmental noise such as oil, wrinkles, dry skin, dirt, displacement etc. The noisy, distorted and/or misaligned fingerprint produced as a 2-D on x-y image, is enhanced and optimized using a hybrid Modified Gabor Filter-Hierarchal Structure Check (MGF-HSC) system model. In face biometric, Fast Principal Component Analysis (FPCA) algorithm was used in which different face conditions (face distortions) such as lighting, blurriness, pose, head orientation and other conditions are addressed. The algorithms used improved the quality of distorted and misaligned fingerprint image. They also improved the recognition accuracy of distorted face during authentication. The results obtained showed that the combination of both fingerprint and face improve the overall performance of biometric authentication system in access control.
Barman et al., (2015) proposed an approach to generate cryptographic key from cancelable fingerprint template of both communicating parties. Cancelable fingerprint templates of both sender and receiver are securely transmitted to each other using a key-based steganography. Both templates are combined with concatenation-based feature level fusion technique and generate a combined template. Elements of combined template are shuffled using shuffle key and hash of the shuffled template generates a unique session key.
In this approach, revocable key for symmetric cryptography is generated from irrevocable fingerprint and privacy of the fingerprints is protected by the cancel able transformation of fingerprint template. Our experimental results show that minimum, average, and maximum Hamming distances between genuine key and impostor’s key are 80, 128, and 168 bits, respectively, with 256-bit cryptographic key. This fingerprint-based cryptographic key can be applied in symmetric cryptography where session based unique key is required.
Abuguba et al., (2015) presented an efficient approach to secure cryptographic key generation from iris and face biometric traits. Features extracted from preprocessed face and iris images are fused at the feature level and the multimodal biometric template is constructed from the Gabor filter and Principal Component Analysis outputs. This template is used to generate strong 256-bit cryptographic key. Experiments were performed using iris and face images from CASIA and ORL databases and the efficiency of the proposed approach is confirmed.
Selvarani and Visu (2015) presented personal identification using fingerprint and Iris biometric technology. Usually unimodal biometric techniques are used. Cloud computing provides many resources, very convenient charged service and minimum cost computing. This leads the cloud computing to become the most dominant computing in the recent years. Even though the cloud provide secured service, it also undergoes with some security problem especially form hackers. The existing unimodal bio cryptography techniques often have limitations such as consciousness to noise, intra class consistency, data aspect and other factors. This research study presents personal identification using fingerprint, iris and cryptographic technologies. Combined biometric technology will secure the data from unauthorized users. The purpose of this study is to study the combination of fingerprint and iris and also to include the cryptographic methods to achieve the higher accuracy and more security. The result of this study can overcome some of the limitations of using single biometric technology. The combination of Finger print and Iris form the key for Blowfish algorithm to store the secured data from unauthorized users in cloud environment.
Oluwadamilola et al., (2017) presented combination of cryptography and biometrics; a bimodal biometric cryptosystem, using fingerprint and face as trait for authentication. Subjects’ information was encrypted using Advanced Encryption Standard (AES) and biometric templates were stored as Binary Large Object (BLOB) in MYSQL database secured with Message Digest 5 (MD 5) Hashing Algorithm. The system was developed and implemented to operate on one-try, two-try and three-try configurations at varying threshold values. The developed system’s performance was evaluated using False Reject Rate (FRR), False Accept Rate (FAR) and Receiver Operating Characteristic Curve (ROC graph) as performance metrics. On ROC graph, three-try configuration gave optimal performance at all threshold values.
Okokpujie et al., (2018) proposed bimodal biometrics (fingerprint and iris) as a means of ensuring the full integrity of the bank’s vault system, thus, further reducing the rate of compromise and theft within the bank’s vault system. A scanner captures the fingerprint and the iris of authorized users. The images of the fingerprint and iris captured by the scanner are segmented, normalized and made into templates that are stored in a database along with the particulars of the users. The accuracy of the system is measured in terms of sample acquisition error and recognition performance using False Accept Rate (FAR), False Identification Rate (FIR) and False Reject Rate (FRR). The result shows that the proposed system is very effective.
Komal (2019) studied a robust multimodal biometric crypto system, in which two modalities (FKP and face) are used for authentication of a person and one modality (fingerprint) is used for key generation. AES algorithm with fingerprint-based key is used for securing the biometric templates. At authentication time, decision level fusion with AND rule is used for making the final decision. The proposed multimodal biometric crypto system is more robust and secure as compare with other multimodal biometric systems.
Venna and Inampudi (2019) developed a multimodal biometric authentication system (MMBAS) using face, fingerprint and retina images and key generation is also done using these images. Images were pre-processed using adaptive median filtering and Otsu’s segmentation algorithm for background subtraction. Then minutiae feature of these images were extracted with the use of Local Binary Pattern (LBP) algorithm and then the feature vectors of face, fingerprint and retina are fused using XOR operation. Later the fused feature vector was used for cryptographic key generation. The evaluation was performed on network security for showing the reliability of the newly introduced approach in terms of Precision, Recall, Accuracy and false rejection rate.
In light of the above reviewed work, Seshadri and Trivedi (2011) described several types of cryptosystems available for biometry applications such as key release, key binding and key generation cryptosystems, most of the researchers majorly focused on key release cryptosystems but paid less attention to key binding cryptosystems, the computational cost of the encryption and decryption of images as well as the required memory space for the encrypted data. Also, it was discovered that some of these techniques were susceptible to attack and were characterized with visual distortions after authentication. Therefore, this work will adopt an enhanced AES technique which focuses on key binding for encryption and decryption for data security and confidentiality. The scheme that will be used in this study, is targeted at ensuring scalable encrypting capacity, good imperceptibility and security performance, and robustness against various attacks with optimal computational efficiency.
The bimodal biometric-cryptography system comprised of quite a few modules to authenticate or verify subjects. The system was divided into two stages; enrolment and authentication as shown in Figure 3.1. In this approach, biometric features were extracted from individual’s face and iris after application of histogram equalization for face and iris, iris localization using Hough transform and iris normalization using Daugman’s rubber sheet model for each modality. Features were extracted using Principal Component Analysis (PCA) while cryptography key was generated using fused features from the face and iris by Advanced Encryption System based Fast Fourier Transform (AES-FFT). The features extracted were fused at feature level using weighted average and optimal features were selected using genetic programming (GP). Support Vector Machine (SVM) was used to classify the extracted features. To enhance the integrity of the system, templates and encrypted data were stored in a database.
Details of the modules are as follows:
A. Enrolment Stage
Enrolment stage comprised of collection of biometric information (face and iris). At this stage, fused features of face and iris were the means for subject to indicate personal identity for authentication. The dataset used contained 600 iris images and 600 face images, 360 of the iris images and 360 of the face images were used in training the model while 240 of the iris images and 240 of the face images were used to test the model used for authentication. The enrolment stage of the system was made up of the sensor module (enrolment module), pre-processing module, feature extraction module, fusion and feature selection module and encryption and decryption module. At this phase, a webcam device was used to acquire face and iris biometric data of users. Face and iris images of 600 subjects with 3 different samples were captured with a size of 640 by 480 pixels. The two biometric traits were downsized into 128 by 128 pixel without any alteration in the images. All images taken had equal uniform illumination conditions and light color background. The database was populated with 1200 images.
A. Conclusion This project evaluated the essential features of unimodal (irises and faces) and multi-modal (fused irises and faces) on the performance of multi-modal crypto-biometric system. Three hundred and sixty (360) irises were trained and two hundred and forty (240) irises were used to test the developed technique at different thresholds. The experimental results obtained revealed that the fused irises and faces under AES-FFT encryption technique with SVM classifier gave 98.33% in terms of recognition accuracies, 0.83% false acceptance rate, 97.50% false rejection rate, 99.17% specificity and 32.99s recognition time compare with irises and faces modality. In view of this, an automated bi-modal crypto-biometric system based on fused irises and faces (that is, both faces and irises), would produce a more reliable accurate and secure system on any repository system as a result of its high accuracy. In other words, the developed AES-FFT technique has ensured scalable encrypting capacity, good imperceptibility and security performance, and robustness against various attacks with optimal computational efficiency in terms of its accuracy and time. The techniques developed in this work proved through empirical evidence to be efficient with reduced complexity and robustness against common attacks. It will be applicable to encrypt confidential or personal data. It will eliminate the flaws, vulnerabilities to attack and threats of adulterating digital content. It can also be useful for security purposes in commercial activities which include physical access control, computer network login, door security system, electronic data security and medical records management. B. Recommendation With regards to the performance of the developed technique, SVM based iris system can be used to enhance security challenges in an automated machine such as ATM. It is recommended that: Some evolutionary search algorithm such as Ant Colony Optimization (ACO), Evolutionary Programming (EP), GLCM (GP), Differential Evolution (DE), Artificial Immune Systems (AIS), can be introduced as feature selection techniques in other to aid recognition process. Other Artificial Neural Network techniques could be compared with SVM in other to determine its computational efficiency on iris systems A computer system with higher configuration and capability should be employed in other to handle more datasets because test-running the system with large dataset took a longer time to process.
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