An enhanced iris recognition and authentication system using energy measure

In order to fight identity fraud, the use of a reliable personal identifier has become a necessity. Using Personal Identification Number (PIN) or a password is no longer secure enough to identify an individual. Iris recognition is considered to be one of the best and accurate form of biometric measurements compared to others, it has become an interesting research area. Iris recognition and authentication has a major issue in its code generation and verification accuracy, in order to enhance the authentication process, a binary bit sequence of iris is generated, which contain several vital information that is used to calculate the Mean Energy and Maximum Energy that goes into the eye with an adopted Threshold Value. The information generated can further be used to find out different eye ailments. An iris is obtained using a predefined iris image which is scanned through eight (8) different stages and wavelet packet decomposition is used to generate 64 wavelet packages bit iris code so as to match the iris codes with Hamming distance criteria and evaluate different energy values. The system showed 98% True Acceptance Rate and 1% False Rejection Rate and this is because some of the irises weren’t properly captured during iris acquisition phase. The system is implemented using UBIRIS v.1 Database.Keywords: Local Image Properties, Authentication Enhancement, Iris Authentication, Local Image, Iris Recognition, Binary Bit Sequenc

Iris feature extraction: a survey

Biometric as a technology has been proved to be a reliable means of enforcing constraint in a security sensitiveenvironment. Among the biometric technologies, iris recognition system is highly accurate and reliable becauseof their stable characteristics throughout lifetime. Iris recognition is one of the biometric identification thatemploys pattern recognition technology with the use of high resolution camera. Iris recognition consist of manysections among which feature extraction is an important stage. Extraction of iris features is very important andmust be successfully carried out before iris signature is stored as a template. This paper gives a comprehensivereview of different fundamental iris feature extraction methods, and some other methods available in literatures.It also gives a summarised form of performance accuracy of available algorithms. This establishes a platform onwhich future research on iris feature extraction algorithm(s) as a component of iris recognition system can bebased.Keywords: biometric authentication, false acceptance rate (FAR), false rejection rate (FRR), feature extraction,iris recognition system

Multi-modal association learning using spike-timing dependent plasticity (STDP)

We propose an associative learning model that can integrate facial images with speech signals to target a subject in a reinforcement learning (RL) paradigm. Through this approach, the rules of learning will involve associating paired stimuli (stimulus–stimulus, i.e., face–speech), which is also known as predictor-choice pairs. Prior to a learning simulation, we extract the features of the biometrics used in the study. For facial features, we experiment by using two approaches: principal component analysis (PCA)-based Eigenfaces and singular value decomposition (SVD). For speech features, we use wavelet packet decomposition (WPD). The experiments show that the PCA-based Eigenfaces feature extraction approach produces better results than SVD. We implement the proposed learning model by using the Spike- Timing-Dependent Plasticity (STDP) algorithm, which depends on the time and rate of pre-post synaptic spikes. The key contribution of our study is the implementation of learning rules via STDP and firing rate in spatiotemporal neural networks based on the Izhikevich spiking model. In our learning, we implement learning for response group association by following the reward-modulated STDP in terms of RL, wherein the firing rate of the response groups determines the reward that will be given. We perform a number of experiments that use existing face samples from the Olivetti Research Laboratory (ORL) dataset, and speech samples from TIDigits. After several experiments and simulations are performed to recognize a subject, the results show that the proposed learning model can associate the predictor (face) with the choice (speech) at optimum performance rates of 77.26% and 82.66% for training and testing, respectively. We also perform learning by using real data, that is, an experiment is conducted on a sample of face–speech data, which have been collected in a manner similar to that of the initial data. The performance results are 79.11% and 77.33% for training and testing, respectively. Based on these results, the proposed learning model can produce high learning performance in terms of combining heterogeneous data (face–speech). This finding opens possibilities to expand RL in the field of biometric authenticatio

Fault diagnosis of rolling bearing based on relevance vector machine and kernel principal component analysis

In order to improve the speed and accuracy of rolling bearing fault diagnosis on small samples, a method based on relevance vector machine (RVM) and Kernel Principle Component Analysis (KPCA) is proposed. Firstly, the wavelet packet energy of the vibration signal is extracted with the wavelet packet transform, which is used as fault feature vectors. Secondly, the dimension of feature vectors is reduced in order to weaken the correlation between the features. The important principal components are selected using KPCA as the new feature vectors under the criterion that the cumulative variance is greater than 95 %. Finally, the faults of rolling bearing are diagnosed through combining KPCA with RVM. Simulation experimental indicates the advantages of the presented method. Moreover, the proposed approach is applied to diagnoses rolling bearing fault. The results show that wavelet packet energy can express rolling bearing fault features accurately, KPCA can reduce the dimension of feature vectors effectively and the proposed method has better performance in the speed of fault diagnosis than the method based on support vector machine (SVM), which supplies a strategy of fault diagnosis for rolling bearing. In this paper, the performance of the proposed method is also compared with other diagnostic methods

Features for Cross Spectral Image Matching: A Survey

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching

Speeded Up Robust Features Descriptor for Iris Recognition Systems

اكتسبت النظم البايومترية اهتماما كبيرا لعدة تطبيقات. كان تحديد القزحية أحد أكثر التقنيات البايومترية تطوراً للمصادقة الفعالة. نظام التعرف على القزحية الحالية يقدم نتائج دقيقة وموثوق بها على أساس الصور المأخوذة بالأشعة التحت الحمراء (NIR) عندما يتم التقاط الصور في مسافة ثابتة مع تعاون المستخدم. ولكن بالنسبة لصور العين الملونة التي تم الحصول عليها تحت الطول الموجي المرئي (VW) دون التعاون بين المستخدمين، فإن كفاءة التعرف على القزحية تتأثر بسبب الضوضاء مثل صور عدم وضوح العين، و تداخل الرموش ، والانسداد  بالأجفان وغيرها. يهدف هذا العمل إلى استخدام (SURF) لاسترداد خصائص القزحية في كل من صور قزحية NIR والطيف المرئي. يتم استخدام هذا النهج وتقييمه على قواعد بيانات CASIA v1and IITD v1 كصورة قزحية NIR وUBIRIS v1 كصورة ملونة. وأظهرت النتائج معدل دقة عالية (98.1 ٪) على CASIA v1, (98.2) على IITD v1 و (83٪) على UBIRIS v1 تقييمها بالمقارنة مع الأساليب الأخرى.Biometric systems have gained significant attention for several applications. Iris identification was one of the most sophisticated biometrical techniques for effective and confident authentication. Current iris identification system offers accurate and reliable results based on near- infra -red light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without cooperation between the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion and reflection. This works aims to use Speeded up robust features Descriptor (SURF) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. This approach is used and evaluated on the CASIA v1and IITD v1 databases as NIR iris image and UBIRIS v1 as color image. The evaluation results showed a high accuracy rate 98.1 % on CASIA v1, 98.2 on IITD v1 and 83% on UBIRIS v1 evaluated by comparing to the other method

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

Eyelid Localization for Iris Identification

This article presents a new eyelid localization algorithm based on a parabolic curve fitting. To deal with eyelashes, low contrast or false detection due to iris texture, we propose a two steps algorithm. First, possible edge candidates are selected by applying edge detection on a restricted area inside the iris. Then, a gradient maximization is applied along every parabola, on a larger area, to refine parameters and select the best one. Experiments have been conducted on a database of 151 iris that have been manually segmented. The performance evaluation is carried out by comparing the segmented images obtained by the proposed method with the manual segmentation. The results are satisfactory in more than 90% of the cases