Multi-Modal Biometrics: Applications, Strategies and Operations

The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented

Performance comparison of intrusion detection systems and application of machine learning to Snort system

This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort

A Survey on Biometrics and Cancelable Biometrics Systems

Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results

Face Recognition: Issues, Methods and Alternative Applications

Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition

Sistem Identifikasi Biometrika Multimodal Palmprint dan Palmvein Menggunakan Two-Dimensional Locality Preserving Projection

               Hingga saat ini sistem biometrika belum menunjukkan kemudahan dan kehandalannya sebagai sistem autentikasi secara sempurna. Tantangan utama pada sistem biometrika yang masih menjadi bahan kajian penelitian yaitu masalah akurasi yang masih perlu ditingkatkan, sistem berskala besar, dan kemampuan sistem beradaptasi terhadap lingkungan yang tidak menentu.                 Kombinasi tepat antara modalitas dan teknik ekstraksi ciri sangat penting dalam merancang sistem biometrika agar menghasilkan performansi sistem yang maksimal. Palmprint dan palmvein muncul sebagai modalitas yang menjanjikan jika digunakan pada sistem biometrika karena keunikan dan kemudahan proses akuisisinya. Banyak penelitian yang telah dilakukan menggunakan berbagai teknik ekstraksi ciri seperti PCA, ICA, LDA, LBP, dan LDP diterapkan pada palmprint maupun palmvein dengan akurasi masing-masing melampaui angka 90%. Pada penelitian Tugas Akhir ini dibahas mengenai skema sistem biometrika multimodal memanfaatkan palmprint dan palmvein secara simultan melanjutkan penelitian sebelumnya yang telah berhasil menerapkan sistem serupa dengan menggabungkan kedua modalitas tersebut di level citra. Algoritma ekstraksi ciri Two-Dimensional Locality Preserving Projection (2DLPP) diterapkan pada palmprint dan palmvein secara terpisah untuk mendapatkan matriks transformasi yang selanjutnya digunakan untuk memproyeksikan citra palmprint maupun palmvein ke ruang dimensi vektor ciri. Nilai kemiripan vektor ciri palmprint dan palmvein antara data model dan data uji dihitung menggunakan Euclidean Distance yang kemudian digabungkan dengan memberikan faktor bobot kepada masing-masing nilai.                 Hasil dari penelitian ini menunjukkan efisiensi dari algoritma ekstraksi ciri 2DLPP dan performansi sistem yang dijabarkan ke dalam beberapa skenario pengujian. Parameter pengujian yang digunakan yaitu panjang dimensi vektor ciri, nilai bobot pada skema penggabungan ciri, dan nilai threshold untuk pengambilan keputusan. Baseline akurasi diukur dengan menggunakan seluruh atribut pada vektor ciri sejumlah 600 atribut dalam proses pencocokan untuk palmprint dan palmvein secara berturut-turut yaitu 89% dan 94,83%. Akurasi dihitung kembali dengan melakukan reduksi terhadap atribut vektor ciri dan menghasilkan panjang vektor ciri optimal untuk palmprint dan palmvein secara berturut-turut yaitu 480 atau 80% dari panjang total dimensi dan 360 atau 60% dari panjang total dimensi. Penggabungan ciri palmprint dan palmvein menggunakan nilai bobot 0,16 dengan maksud memberi level kepercayaan sebesar 16% kepada ciri palmprint dan 84% kepada ciri palmvein untuk skema multimodal dan menghasilkan akurasi sebesar 95,83%. Nilai threshold optimal yang didapatkan yaitu 295,2073 dengan performansi berupa recognition rate maksimum yang mampu dicapai dalam hal verifikasi dan identifikasi secara berturut-turut yaitu 94,67% dan 97,33%. __Kata kunci:__ biometrika, multimodal, region of interest, two-dimensional locality preserving projectio

Robust Image Recognition Based on a New Supervised Kernel Subspace Learning Method

Fecha de lectura de Tesis Doctoral: 13 de septiembre 2019Image recognition is a term for computer technologies that can recognize certain people, objects or other targeted subjects through the use of algorithms and machine learning concepts. Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only is a nonlinear and complex variation of face images effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. In this research, we particularly focus on face recognition however, two other types of databases rather than face databases are also applied to well investigate the implementation of our algorithm. Experimental results reveal that our method consistently outperforms its competitors across a wide range of dimensionality on all the datasets. SKLDNE method has reached 100 percent of recognition rate for Tn=17 on the Sheffield, 9 on the Yale, 8 on the ORL, 7 on the Finger vein and 11on the Finger Knuckle respectively, while the results are much lower for other methods. This demonstrates the robustness and effectiveness of the proposed method

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithm

Palmprint identification using restricted fusion

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