A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features

In this paper we propose a novel visual method for protein model classification and retrieval. Different from the conventional methods, the key idea of the proposed method is to extract image features of proteins and measure the visual similarity between proteins. Firstly, the multiview images are captured by vertices and planes of a given octahedron surrounding the protein. Secondly, the local features are extracted from each image of the different views by the SURF algorithm and are vector quantized into visual words using a visual codebook. Finally, KLD is employed to calculate the similarity distance between two feature vectors. Experimental results show that the proposed method has encouraging performances for protein retrieval and categorization as shown in the comparison with other methods

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

Recognition using SIFT and its Variants on Improved Segmented Iris

Iris is one of the most reliable biometric traits due to its stability and randomness. Iris is transformed to polar coordinates by the conventional recognition systems. They perform well for the cooperative databases, but the performance deteriorates for the non-cooperative irises. In addition to this, aliasing effect is introduced as a result of transforming iris to polar domain. In this thesis, these issues are addressed by considering annular iris free from noise due to eyelids. This thesis presents several SIFT based methods for extracting distinctive invariant features from iris that can be used to perform reliable matching between different views of an object or scene. After localization of the iris, Scale Invariant Feature Transform (SIFT) is used to extract the local features. The SIFT descriptor is a widely used method for matching image features. But SIFT is found out to be computationally very complex. So we use another keypoint descriptor, Speeded up Robust Features (SURF), which is found to be computationally more efficient and produces better results than the SIFT. Both SIFT and SURF has the problem of false pairing. This has been overcome by using Fourier transform with SIFT (called F-SIFT) to obtain the keypoint descriptor and Phase-Only Correlation for feature matching. F-SIFT was found to have better accuracy than both SIFT and SURF as the problem of false pairing is significantly reduced. We also propose a new method called S-SIFT where we used S Transform with SIFT to obtain the keypoint descriptor for the image and Phase-Only Correlation for the feature matching. In the thesis we provide a comparative analysis of these four methods (SIFT, SURF, F-SIFT, S-SIFT) for feature extraction in iris

Comparision of Iris Identification by Using modified SIFT and SURF keypoint Descriptor

A wide variety of systems require reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that only a legitimate user, access the rendered service. A biometrics system is essentially a pattern recognition system, which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristic possessed by the user. Iris serves as one of the excellent biometric traits due to the stability and randomness of its unique features. After localization of the iris, Scale Invariant Feature Transform (SIFT) is used to extract the local features. But SIFT is found out to be computational complex.So in this paper another keypoint descriptor ,Speeded Up Robust Features (SURF), is tested and then modified which compare the performance of different descriptor and hence gives promising results with very less computations. We finally carry out a comparision of both the descriptors performance wise

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Iris Identification using Keypoint Descriptors and Geometric Hashing

Iris is one of the most reliable biometric trait due to its stability and randomness. Conventional recognition systems transform the iris to polar coordinates and perform well for co-operative databases. However, the problem aggravates to manifold for recognizing non-cooperative irises. In addition, the transformation of iris to polar domain introduces aliasing effect. In this thesis, the aforementioned issues are addressed by considering Noise Independent Annular Iris for feature extraction. Global feature extraction approaches are rendered as unsuitable for annular iris due to change in scale as they could not achieve invariance to ransformation and illumination. On the contrary, local features are invariant to image scaling, rotation and partially invariant to change in illumination and viewpoint. To extract local features, Harris Corner Points are detected from iris and matched using novel Dual stage approach. Harris corner improves accuracy but fails to achieve scale invariance. Further, Scale Invariant Feature Transform (SIFT) has been applied to annular iris and results are found to be very promising. However, SIFT is computationally expensive for recognition due to higher dimensional descriptor. Thus, a recently evolved keypoint descriptor called Speeded Up Robust Features (SURF) is applied to mark performance improvement in terms of time as well as accuracy. For identification, retrieval time plays a significant role in addition to accuracy. Traditional indexing approaches cannot be applied to biometrics as data are unstructured. In this thesis, two novel approaches has been developed for indexing iris database. In the first approach, Energy Histogram of DCT coefficients is used to form a B-tree. This approach performs well for cooperative databases. In the second approach, indexing is done using Geometric Hashing of SIFT keypoints. The latter indexing approach achieves invariance to similarity transformations, illumination and occlusion and performs with an accuracy of more than 98% for cooperative as well as non-cooperative databases

Optimized biometric system based iris-signature for human identification

This research aimed at comparing iris-signature techniques, namely the Sequential Technique (ST) and the Standard Deviation Technique (SDT). Both techniques were measured by Backpropagation (BP), Probabilistic, Radial basis function (RBF), and Euclidian distance (ED) classifiers. A biometric system-based iris is developed to identify 30 of CASIA-v1 and 10 subjects from the Real-iris datasets. Then, the proposed unimodal system uses Fourier descriptors to extract the iris features and represent them as an iris-signature graph. The 150 values of input machine vector were optimized to include only high-frequency coefficients of the iris-signature, then the two optimization techniques are applied and compared. The first optimization (ST) selects sequentially new feature values with different lengths from the enrichment graph region that has rapid frequency changes. The second technique (SDT) chooses the high variance coefficients as a new feature of vectors based on the standard deviation formula. The results show that SDT achieved better recognition performance with the lowest vector-lengths, while Probabilistic and BP have the best accuracy

Development of Robust Iris Localization and Impairment Pruning Schemes

Iris is the sphincter having flowery pattern around pupil in the eye region. The high randomness of the pattern makes iris unique for each individual and iris is identified by the scientists to be a candidate for automated machine recognition of identity of an individual. The morphogenesis of iris is completed while baby is in mother's womb; hence the iris pattern does not change throughout the span of life of a person. It makes iris one of the most reliable biometric traits. Localization of iris is the first step in iris biometric recognition system. The performance of matching is dependent on the accuracy of localization, because mislocalization would lead the next phases of biometric system to malfunction. The first part of the thesis investigates choke points of the existing localization approaches and proposes a method of devising an adaptive threshold of binarization for pupil detection. The thesis also contributes in modifying conventional integrodifferential operator based iris detection and proposes a modified version of it that uses canny detected edge map for iris detection. The other part of the thesis looks into pros and cons of the conventional global and local feature matching techniques for iris. The review of related research works on matching techniques leads to the observation that local features like Scale Invariant Feature Transform(SIFT) gives satisfactory recognition accuracy for good quality images. But the performance degrades when the images are occluded or taken non-cooperatively. As SIFT matches keypoints on the basis of 128-D local descriptors, hence it sometimes falsely pairs two keypoints which are from different portions of two iris images. Subsequently the need for filtering or pruning of faulty SIFT pairs is felt. The thesis proposes two methods of filtering impairments (faulty pairs) based on the knowledge of spatial information of the keypoints. The two proposed pruning algorithms (Angular Filtering and Scale Filtering) are applied separately and applied in union to have a complete comparative analysis of the result of matching