A three dimensional imaging-based framework for planning maxillomandibular advancement surgery for the treatment of obstructive sleep apnoea
- Authors: Islam, Syed , Goonewardene, Mithran , Bennamoun, Mohammed , Lucey, Anthony , Farella, Mauro , Abduo, Jaafar , Cisonni, Julien
- Date: 2013
- Type: Text , Conference proceedings , Conference paper
- Relation: 2013 4th World Congress on Software Engineering, WCSE 2013; Hong Kong, China; 3rd-4th December 2013 p. 301-306
- Full Text: false
- Reviewed:
- Description: Obstructive Sleep Apnoea (OSA), a sleeping disorder, is a serious health issue with significant public health implications. Due to the interrupted sleep, OSA patients suffer with excessive day-time sleepiness, fatigue and other health complexities that lead to on-road and work-related accidents and incur billions of dollars per year. Traditionally, treatment of OSA begins with nasal continuous positive airway pressure (CPAP). Alternatively, Mandibular Repositioning Appliances or surgical interventions can be used. Although Maxillomandibular Advancement (MMA) surgery is often advised as the last line of treatment due to the expense and significant changes in the facial appearance, it is the only permanent solution to OSA with a definitive outcome especially for the patients with significant facial deformation or anomalies. In this article, three dimensional (3D) image-guided predictive algorithms are proposed to improve the treatment planning and overall outcome of the MMA surgery. 3D analysis of the facial surface data and Computational Biomechanics-based 3D modelling of airway segmented from Cone Beam Computed Tomography (CBCT) data are proposed to predict the required physiological changes to ensure optimal air-flow through the airway. Moreover, 3D Computer Graphics-based techniques are proposed to visualise and demonstrate the expected facial outcomes to inform patients and surgeons prior to this non-reversible surgery.
A three-phase half-bridge cascaded inverter with reduced number of input DC supply
- Authors: Hasan, Mubashwar , Abu-Siada, Ahmed , Islam, Syed , Muyeen, S.
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 Australasian Universities Power Engineering Conference, AUPEC 2017; Melbourne, Australia; 19th-22nd November 2017 Vol. 2017, p. 1-7
- Full Text: false
- Reviewed:
- Description: Cascaded multilevel inverters (MLI) have recently received much attention due to its ability to perform well in various high voltage and high power applications with high efficiency. Cascaded inverters are able to generate high voltage output by utilizing a number of low voltage DC supplies and switches of low blocking voltage rating, which make cascaded MLI a cost effective choice for high voltage/power applications. The main drawback of cascaded MLI is the requirement of large number of isolated DC sources particularly, for three phase applications where the number of required input DC sources is three times that of single phase structure. In addition to the extra cost it will incur, the use of large number of DC supplies within the inverter will significantly increase its physical size, and complicate the management of such large number of DC sources. This paper presents a new topology for three phase MLI with a minimum number of input DC supplies. Symmetric and asymmetric input DC supply modes are developed for the proposed topology. Simulation and experimental results are provided to assess the performance of the proposed MLI topology.
A trade-off between reliability and energy efficiency for inter-cluster communication in wireless sensor networks
- Authors: Sadat, Anwar , Karmakar, Gour
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
A travelling wave detector based fault location device and data recorder for medium voltage distribution systems
- Authors: Jahromi, Ali , Wolfs, Peter , Islam, Syed
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2016 Australasian Universities Power Engineering Conference, AUPEC 2016; Brisbane, Australia; 25th-28th September 2016 p. 1-5
- Full Text: false
- Reviewed:
- Description: This paper presents a hardware design for a Travelling Wave (TW) Detector and data recorder for a three phase Medium Voltage (MV) distribution network. The proposed pole mounted platform consists of a capacitively coupled receiver system, a GPS receiver and a Texas Instruments Delfino 28377 processor based travelling wave detection unit. The data recording system uses an Intel Atom base single board computer, a four channel 10Ms/s analogue to digital converter card along with Wi-Fi and GORS communications links. The proposed system is capable of recording three phase voltages simultaneously with the ability to trigger remotely. The platform is mounted in an IP56 enclosure and can be mounted on the MV distribution poles. The paper provides a brief review of hardware and software developed for the TW detector.
A triangulation-based technique for building boundary identification from point cloud data
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015; Auckland, New Zealand; 23rd-24th November 2015 Vol. 2016-November, p. 1-6
- Full Text: false
- Reviewed:
- Description: Building boundary identification is an essential prerequisite in building outline generation from point cloud data. In this problem, boundary edges that constitute the building boundary are identified. The existing solutions to the identification of boundary edges from the input point set have one or more of the following problems: ineffective in finding appropriate edges in a concave shape, incapable of determining a 'hole' or 'concavity' inside the shape separately, dependant on additional information such as the scan direction that may be unavailable, and incompetent in determining the boundary of a point set from the boundaries of two or more subsets of the point set. This paper proposes a new solution to the identification of building boundary by using the maximum point-to-point distance in the input data. It properly detects the boundary edges for any type of shape and separately recognises holes, if any, inside the shape. The unique feature of the proposed solution is that it can identify the boundary of a point set from the boundaries of two or more subsets of the point set. It does not require any additional information other than the input point set. Experimental results show that the proposed solution can preserve details along the building boundary and offer high area-based completeness and quality, even in low density input data. © 2015 IEEE.
- Description: International Conference Image and Vision Computing New Zealand
A weighted overlook graph representation of eeg data for absence epilepsy detection
- Authors: Wang, Jialin , Liang, Shen , Wang, Ye , Zhang, Yanchun , Ma, Jiangang
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 20th IEEE International Conference on Data Mining, ICDM 2020 Vol. 2020-November, p. 581-590
- Full Text: false
- Reviewed:
- Description: Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances. © 2020 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate "Jiangang Ma“ is provided in this record**
Abnormal event detection in unseen scenarios
- Authors: Haque, Mahfuzul , Murshed, Manzur
- Date: 2012
- Type: Text , Conference proceedings
- Relation: 2012 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Melbourne, 9-13th July, 2012. pg 1-6
- Full Text: false
- Reviewed:
- Description: Event detection in unseen scenarios is a challenging problem due to high variability of scene type, viewing direction, nature of scene entities, and environmental conditions. Existing event detection approaches mostly rely on context-specific tuning and training. Consequently, these techniques fail to achieve high scalability in a large surveillance network with hundreds of video feeds where scenario specific tuning/training is impossible. In this paper, we present a generic event detection approach where the extracted low-level features represent the global characteristics of the target scene instead of any context-specific information. From the temporal evolution of these context-invariant features over a timeframe, a fixed number of temporal features are extracted based on the periodicity of significant transition points and associated temporal orders. Finally, top-ranked temporal features are used to train binary classifier-based event models. In this approach, supervised training and exhaustive feature extraction are required only once while building the target event models. During real-time operation in unseen scenarios, event detection is performed based on the trained event models by extracting the required features only. The proposed event detection approach has been demonstrated for abnormal event detection in completely unseen public place scenarios from benchmark datasets without additional training and tuning. Furthermore, the proposed event detection approach has also outperformed recent optical flow based event detection technique.
Acoustic sensor networks and mobile robotics for sound source localization
- Authors: Nguyen, Linh , Miro, Jaime Valls
- Date: 2019
- Type: Text , Conference proceedings
- Relation: IEEE 15th International Conference on Control and Automation (ICCA);Edinburgh, UK; 16-19 July 2019 p. 1453-1458
- Full Text: false
- Reviewed:
- Description: Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising.
Action recognition using spatio-temporal distance classifier correlation filter
- Authors: Anwaar-Ul Haq , Gondal, Iqbal , Murshed, Manzur
- Date: 2011
- Type: Text , Conference proceedings
- Relation: 2011 International Conference on Digital Image Computing Techniques and Applications (DICTA), Noosa, QLD, 6th-8th Dec, 2011
- Full Text: false
- Reviewed:
- Description: The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.
- Description: The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like \emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a \emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.
Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions
- Authors: Ulhaq, Anwaar , Yin, Xiaoxia , Zhang, Yunchan , Gondal, Iqbal
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016; Lecce, Italy; 24th-27th October 2016; published in Advanced Conepts for Intelligent Vision Systems (Lecture Notes in Computer Science series) Vol. 10016 LNCS, p. 465-476
- Full Text: false
- Reviewed:
- Description: Human actions are spatio-temporal visual events and recognizing human actions in different conditions is still a challenging computer vision problem. In this paper, we introduce a robust feature based space-time correlation filter, called Action-02MCF (0’zero-aliasing’ 2M’ Maximum Margin’) for recognizing human actions in video sequences. This filter combines (i) the sparsity of spatio-temporal feature space, (ii) generalization of maximum margin criteria, (iii) enhanced aliasing free localization performance of correlation filtering using (iv) rich context of maximally stable space-time interest points into a single classifier. Its rich multi-objective function provides robustness, generalization and recognition as a single package. Action-02MCF can simultaneously localize and classify actions of interest even in clutter and adverse imaging conditions. We evaluate the performance of our proposed filter for challenging human action datasets. Experimental results verify the performance potential of our action-filter compared to other correlation filtering based action recognition approaches. © Springer International Publishing AG 2016.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Active model selection for positive unlabeled time series classification
- Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 36th IEEE International Conference on Data Engineering, ICDE 2020 Vol. 2020-April, p. 361-372
- Full Text: false
- Reviewed:
- Description: Positive unlabeled time series classification (PUTSC) refers to classifying time series with a set PL of positive labeled examples and a set U of unlabeled ones. Model selection for PUTSC is a largely untouched topic. In this paper, we look into PUTSC model selection, which as far as we know is the first systematic study in this topic. Focusing on the widely adopted self-training one-nearest-neighbor (ST-1NN) paradigm, we propose a model selection framework based on active learning (AL). We present the novel concepts of self-training label propagation, pseudo label calibration principles and ultimately influence to fully exploit the mechanism of ST-1NN. Based on them, we develop an effective model performance evaluation strategy and three AL sampling strategies. Experiments on over 120 datasets and a case study in arrhythmia detection show that our methods can yield top performance in interactive environments, and can achieve near optimal results by querying very limited numbers of labels from the AL oracle. © 2020 IEEE.
- Description: E1
Adaptive clustering with feature ranking for DDoS attacks detection
- Authors: Zi, Lifang , Yearwood, John , Wu, Xin
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second, the Modified Global K-means algorithm (MGKM) is used as the basic incremental clustering algorithm to identify the cluster structure of the target data. Third, the linear correlation coefficient is used for feature ranking. Lastly, the feature ranking result is used to inform and recalculate the clusters. This adaptive process can make worthwhile adjustments to the working feature vector according to different patterns of DDoS attacks, and can improve the quality of the clusters and the effectiveness of the clustering algorithm. The experimental results demonstrate that our method is effective and adaptive in detecting the separate phases of DDoS attacks. © 2010 IEEE.
Adaptive low-power wireless sensor network architecture for smart street furniture-based crowd and environmental measurements
- Authors: Nassar, Mohammed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis , IEEE
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE 20th International Symposium on a World of Wireless, Mobile and Multimedia Networks
- Full Text: false
- Reviewed:
- Description: Street furniture such as bins, seats and bus shelters can become "smart" with the inclusion of wireless sensor nodes, which consist of environmental sensors, wireless modules, processors and microcontrollers. One of the most crucial challenges for smart street furniture is how to manage power consumption efficiently without affecting data freshness. In this work, we propose a novel Wireless Sensor Network (WSN) architecture for smart street furniture. Unlike existing WSNs which are based on a one-way communication model between wireless sensor nodes and the server, the proposed architecture employs a two-way communication model and a dynamic adaptation of the time interval of measurements to balance between power consumption and data updates. Our approach also provides a real-time low-power design for wireless sensor nodes which efficiently communicate the updated data instead of sending the same data on a regular basis. To the best of our knowledge, this is the first work in the relevant literature which extends the functionality of the wireless module in wireless sensor nodes to act not only as a station sending environmental data but also as soft Access Point (AP), sensing MAC addresses and WiFi signal strengths from surrounding WiFi-enabled devices. We have conducted experiments on the Murdoch University campus and our results show that our proposal improves lifetime of wireless sensor nodes up to 293% compared to static architectures similar to the ones that have been proposed in the literature. Moreover, network bandwidth is improved up to 38% without affecting data freshness. Finally, storage space for the database at the server is reduced up to 99%.
- Description: E1
Adaptive sliding-mode dynamic controller for nonholonomic mobile robots
- Authors: Amer, Ahmed , Sallam, Elsayed , Sultan, Ibrahim
- Date: 2016
- Type: Text , Conference proceedings
- Relation: ICENCO 2016 : 12th International Computer Engineering Conference (ICENCO) "Boundless smart societies" ; 2016, Egypt; 28th-29th Dec. 2016 p. 230-235
- Full Text: false
- Reviewed:
- Description: This paper presents a proposed adaptive technique for nonholonomic wheeled mobile robot (NWMR) using the sliding-mode control (SMC) method. The proposed control system based on the backstepping kinematic controller and PI sliding mode dynamic control. With an adaptive fuzzy logic to adjust adaptation gain of SMC for trajectory tracking control of nonholonomic mobile robot. Parametric and nonparametric uncertainties of mobile robot can be solved by using the proposed control which take advantages of stability and robustness in sliding mode control. The adaptation gain of SMC is adjusted by using Mamdani type inference system with adaptive tuning algorithm, which improves the adaptability for uncertainness and eliminate input chattering of the SMC. The stability and convergence of the control system are proved using Lypanouve criteria, and the comparison of the proposed controller with the other controllers ensures the validity and superiority of my own controller
Addressing the confidentiality and integrity of assistive care loop framework using wireless sensor networks
- Authors: Balasubramanian, Venki , Hoang, Doan , Zia, Tanveer
- Date: 2011
- Type: Text , Conference proceedings
- Relation: 21st International Conference on Systems Engineering, ICSEng 2011; Las Vegas, NV; United States; 16th-18th Aug, published in Proceedings - ICSEng 2011: International Conference on Systems Engineering; p. 416-421
- Full Text: false
- Reviewed:
- Description: In-house healthcare monitoring applications are continuous time-critical applications often built upon Body Area Wireless Sensor Networks (BAWSNs). Our Assistive Care Loop Framework (ACLF) is an in-house healthcare application capable of monitoring the health conditions of aged/patients over a dedicated period of time by deploying the BAWSN as the monitoring component. However, the wireless medium used in the BAWSN for communications is prone to vulnerabilities that could open a door to attackers tampering with or compromising the user's data privacy. Hence, it is imperative to maintain the privacy and integrity of the data to gain the confidence and hence, the acceptance of the users of the healthcare applications. Furthermore, in time-critical applications, the vital health conditions must be monitored at regular intervals within their specified critical time. Therefore, the security model proposed for the BAWSN must not incur undue overheads when meeting the critical time requirements of the application. In this paper, we propose and implement a secure adaptive triple-key scheme (aTKS) for the BAWSN to achieve the privacy and integrity of the monitored data with minimal overheads. We then present the performance results of our scheme for the BAWSN, using real-time test-bed implementations and simulations. © 2011 IEEE.
- Description: Proceedings - ICSEng 2011: International Conference on Systems Engineering
Advances in canonical duality theory with applications to global optimization
- Authors: Gao, David
- Date: 2008
- Type: Text , Conference proceedings
- Relation: FOCAPO 2008, Boston, June 29th-July 02, Published in Proceedings of the Fifth International Conference Foundations of Computer-Aided Process Operations pg. 73-82 p. 73-81
- Full Text: false
- Reviewed:
AFES: An advanced forensic evidence system
- Authors: Black, Paul , Gondal, Iqbal , Brooks, Richard , Yu, Lu
- Date: 2021
- Type: Text , Conference proceedings
- Relation: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), Gold Coast, Australia, 25-29th October, 2021 p. 67-74
- Full Text: false
- Reviewed:
- Description: News media often contain reports that raise doubt related to policing operations. We examine the question of how to improve policing integrity during the execution of search warrants and provide an outline for law enforcement search warrants and digital forensic analysis procedures. Existing techniques for improving the integrity of search warrants are reviewed, limitations are noted, and we propose an Advanced Forensic Evidence System (AFES) to address these limitations.AFES provides an immutable record and biometric authentication of the officers present during the execution of a search warrant, time and location, video recording, seizure record, contemporaneous notes, and photographs. AFES records digital evidence items, imaging details, evidence hashes, provides an access control system, and an immutable record of access to all stored items. AFES uses a permissioned distributed ledger prototype, called Scrybe, developed under NSF aegis, to ensure evidence seizure integrity. Scrybe is run as multiple blockchain instances at law enforcement, prosecution, judicial, and defence organisations to ensure that an immutable record is maintained.
Agoraphilic navigation algorithm in dynamic environment with and without prediction of moving objects location
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
- Full Text:
- Reviewed:
- Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
- Description: E1
Alignment-free cancellable template generation for fingerprint based authentication
- Authors: Nazmul, Rumana , Islam, Rafiqul , Chowdhury, Ahsan
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 4th International Conference on Information Systems Security and Privacy, ICISSP 2018; Madeira, Portugal; 22nd-24th January 2018 Vol. 2018-January, p. 361-366
- Full Text: false
- Reviewed:
- Description: With the emergence and extensive deployment of biometric based user authentication system, ensuring the security of biometric template is becoming a growing concern in research community. One approach of securing biometric data is cancellable biometric which transforms the original biometric features into a non-invertible form for enrolment and matching. However, most of the schemes for generating cancellable template are alignment-based requiring an accurate alignment of query and enrolled images, which is very difficult to achieve. In this paper, we propose an alignment-free technique for generating revocable fingerprint template that exploits the local features i.e., minutiae details in a fingerprint image. A rotation and translation invariant values are extracted from the neighbouring region of each minutia. The invariant values are then used as inputs in a transformation function and combined with a stored and a user-specific key based random vectors using the type and orientation information of the minutiae. Hence, by varying the stored and user-specific keys in the transformation, multiple application-specific templates can be generated to preserve users’ privacy. Besides, if the transformed template is compromised, a new template can be reissued by assigning different keys for transformation to achieve revocability. Furthermore, the proposed approach preserves the actual geometric relationships between the enrolled and query templates even after transformation and offers reasonable recognition rate. Experiments conducted on FVC2000 DB1 demonstrate that the proposed method exhibits promising performance in terms of recognition accuracy, computational complexity, security along with diversity, revocability and non-invertibility that are the key issues of cancellable template generation.
- Description: ICISSP 2018 - Proceedings of the 4th International Conference on Information Systems Security and Privacy
Alignment-free fingerprint template protection technique based on minutiae neighbourhood information
- Authors: Nazmul, Rumana , Islam, Rafiqul , Chowdhury, Ahsan
- Date: 2018
- Type: Text , Conference proceedings
- Relation: International Conference on Applications and Techniques in Cyber Security and Intelligence, ATCSI 2017; Ningbo, China; 16th-18th June 2017; published in International Conference on Applications and Techniques in Cyber Security and Intelligence : Applications and Techniques in Cyber Security and Intelligence (Advances in Intelligent Systems and Computing series) Vol. 580, p. 256-265
- Full Text: false
- Reviewed:
- Description: With the emergence and extensive deployment of biometric-based user authentication system, ensuring the security of biometric template is becoming a growing concern in the research community. One approach to securing template is to transform the original biometric features into a non-invertible form and to use it for a person’s authentication. Registration-based template protection schemes require an accurate alignment of the enrolled and the query images, which is very difficult to achieve. To overcome the alignment issue, registration-free template protection approaches have been proposed that rely on local features such as minutiae details in a fingerprint image. In this paper, we develop an alignment-free fingerprint template protection technique which extracts the rotation and translation invariant features from the neighbouring region of each minutia and then exploits the neighbourhood information to achieve the non-invertible property. Evaluation of the proposed scheme on FVC2002 DB1-B shows that the new method exhibits satisfactory performance in terms of recognition accuracy, computational complexity, and security. © 2018, Springer International Publishing AG.