Signal processing and machine learning techniques for human verification based on finger textures

PhD ThesisIn recent years, Finger Textures (FTs) have attracted considerable attention as potential biometric characteristics. They can provide robust recognition performance as they have various human-speci c features, such as wrinkles and apparent lines distributed along the inner surface of all ngers. The main topic of this thesis is verifying people according to their unique FT patterns by exploiting signal processing and machine learning techniques. A Robust Finger Segmentation (RFS) method is rst proposed to isolate nger images from a hand area. It is able to detect the ngers as objects from a hand image. An e cient adaptive nger segmentation method is also suggested to address the problem of alignment variations in the hand image called the Adaptive and Robust Finger Segmentation (ARFS) method. A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction method is proposed which combines the Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). Moreover, an enhanced method called the Enhanced Local Line Binary Pattern (ELLBP) is designed to e ciently analyse the FT patterns. As a result, a powerful human veri cation scheme based on nger Feature Level Fusion with a Probabilistic Neural Network (FLFPNN) is proposed. A multi-object fusion method, termed the Finger Contribution Fusion Neural Network (FCFNN), combines the contribution scores of the nger objects. The veri cation performances are examined in the case of missing FT areas. Consequently, to overcome nger regions which are poorly imaged a method is suggested to salvage missing FT elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN). Finally, a novel method to produce a Receiver Operating Characteristic (ROC) curve from a PNN is suggested. Furthermore, additional development to this method is applied to generate the ROC graph from the FCFNN. Three databases are employed for evaluation: The Hong Kong Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from the CASIA Multi-Spectral (CASIAMS) databases. Comparative simulation studies con rm the e ciency of the proposed methods for human veri cation. The main advantage of both segmentation approaches, the RFS and ARFS, is that they can collect all the FT features. The best results have been benchmarked for the ELLBP feature extraction with the FCFNN, where the best Equal Error Rate (EER) values for the three databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage approach for the missing feature elements has the capability to enhance the veri cation performance for the FLFPNN. Moreover, ROC graphs have been successively established from the PNN and FCFNN.the ministry of higher education and scientific research in Iraq (MOHESR); the Technical college of Mosul; the Iraqi Cultural Attach e; the active people in the MOHESR, who strongly supported Iraqi students

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

Fusion of geometric and texture features for finger knuckle surface recognition

AbstractHand-based biometrics plays a significant role in establishing security for real-time environments involving human interaction and is found to be more successful in terms of high speed and accuracy. This paper investigates on an integrated approach for personal authentication using Finger Back Knuckle Surface (FBKS) based on two methodologies viz., Angular Geometric Analysis based Feature Extraction Method (AGFEM) and Contourlet Transform based Feature Extraction Method (CTFEM). Based on these methods, this personal authentication system simultaneously extracts shape oriented feature information and textural pattern information of FBKS for authenticating an individual. Furthermore, the proposed geometric and textural analysis methods extract feature information from both proximal phalanx and distal phalanx knuckle regions (FBKS), while the existing works of the literature concentrate only on the features of proximal phalanx knuckle region. The finger joint region found nearer to the tip of the finger is called distal phalanx region of FBKS, which is a unique feature and has greater potentiality toward identification. Extensive experiments conducted using newly created database with 5400 FBKS images and the obtained results infer that the integration of shape oriented features with texture feature information yields excellent accuracy rate of 99.12% with lowest equal error rate of 1.04%

A Review Of Multilevel Multibiometric Fusion System

Biometric systems allow automatic person recognition and authenticate based on the physical or behavioral characteristic. In recent years, researchers have paid close attention to the design of efficient multi-modal biometric systems due to their ability to withstand spoof attacks. Sometimes single biometric traits fail to extract relevant information for verifying the identity of a person. Therefore, combining multiple modalities, enhanced performance reliability could be achieved. If the security level increases then multi-level fusion techniques are used. This paper discusses the many fusion levels: algorithms, level of fusion, methods used for integrating the multiple verifiers and their applications

The Method of Automatic Knuckle Image Acquisition for Continuous Verification Systems

The paper proposes a method of automatic knuckle image acquisition for continuous verification systems. The developed acquisition method is dedicated for verification systems in which the person being verified uses a computer keyboard. This manner of acquisition enables registration of the knuckle image without interrupting the user’s work for the time of acquisition. This is an important advantage, unprecedented in the currently known methods. The process of the automatic location of the finger knuckle can be considered as a pattern recognition approach and is based on the analysis of symmetry and similarity between the reference knuckle patterns and live camera image. The effectiveness of the aforesaid approach has been tested experimentally. The test results confirmed its high effectiveness. The effectiveness of the proposed method was also determined in a case where it is a part of a multi-biometric method

Humans Verification by Adopting Deep Recurrent Fingerphotos Network

يمكن اعتبار صورة الإصبع واحدة من أحدث وأكثر التقنيات البيومترية إثارة للاهتمام. يعني ذلك ببساطة صورة بصمة أصبع يتم الحصول عليها عن طريق هاتف ذكي بطريقة لا تتطلب الاتصال المباشر. يقترح هذا البحث نهجًا جديدًا للتحقق من البشر استنادًا إلى صورة الإصبع الفوتوغرافية. يُطلق عليه اسم شبكة الإصبع الفوتوغرافية العميقة المتكررة. تتألف من طبقة الإدخال، وسلسلة من الطبقات الخفية، وطبقة الإخراج والتغذية العكسية الاساسية. يعتمد هذا البحث على اخذ صور فوتوغرافية لكافة الاصابع الشخصية بشكل متسلسل. و يتمتع النظام بالقدرة على التبديل بين أوزان كل إصبع فوتوغرافي فردي وتوفير التحقق. تم انشاء قاعدة بينات من عدد كبير من صور الأصابع الفوتوغرافية، وتم تنظيمها وتقسيمها واستخدامها كمجموعة بيانات مفيدة في هذا البحث. تم التوصل الى نتائج عالية في الدقة  في التحقق الشخصي عن طريق استخدام الصور الفوتوغرافية للاصابع.Fingerphoto can be considered as one of recent and interesting biometrics. It basically means a fingerprint image that is acquired by a smartphone in contactless manner. This paper proposes a new Deep Recurrent Learning (DRL) approach for verifying humans based on their fingerphoto image. It is called the Deep Recurrent Fingerphotos Network (DRFN). It compromises of input layer, sequence of hidden layers, output layer and essential feedback. The proposed DRFN sequentially accepts fingerphoto images of all personal fingers. It has the capability to change between the weights of each individual fingerphoto and provide verification. A huge number of fingerphoto images have been acquired, arranged, segmented and utilized as a useful dataset in this paper. It is named the Fingerphoto Images of Ten Fingers (FITF) dataset. Average accuracy result of 99.84 % is obtained for personal verification by exploiting fingerphotos

Patterns Identification of Finger Outer Knuckles by Utilizing Local Directional Number

Finger Outer Knuckle (FOK) is a distinctive biometric that has grown in popularity recently. This results from its inborn qualities such as stability, protection, and specific anatomical patterns. Applications for the identification of FOK patterns include forensic investigations, access control systems, and personal identity. In this study, we suggest a method for identifying FOK patterns using Local Directional Number (LDN) codes produced from gradient-based compass masks. For the FOK pattern matching, the suggested method uses two asymmetric masks—Kirsch and Gaussian derivative—to compute the edge response and extract LDN codes. To calculate edge response on the pattern, an asymmetric compass mask made from the Gaussian derivative mask is created by rotating the Kirsch mask by 45 degrees to provide edge response in eight distinct directions. The edge response of each mask and the combination of dominating vector numbers are examined during the LDN code-generating process. A distance metric can be used to compare the LDN code\u27s condensed representation of the FOK pattern to the original for matching purposes. On the Indian Institute of Technology Delhi Finger Knuckle (IITDFK) database, the efficiency of the suggested procedure is assessed. The data show that the suggested strategy is effective, with an Equal Error Rate (EER) of 10.78%. This value performs better than other EER values when compared to different approaches

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models