Multimodal biometrics scheme based on discretized eigen feature fusion for identical twins identification

The subject of twins multimodal biometrics identification (TMBI) has consistently been an interesting and also a valuable area of study. Considering high dependency and acceptance, TMBI greatly contributes to the domain of twins identification in biometrics traits. The variation of features resulting from the process of multimodal biometrics feature extraction determines the distinctive characteristics possessed by a twin. However, these features are deemed as inessential as they cause the increase in the search space size and also the difficulty in the generalization process. In this regard, the key challenge is to single out features that are deemed most salient with the ability to accurately recognize the twins using multimodal biometrics. In identification of twins, effective designs of methodology and fusion process are important in assuring its success. These processes could be used in the management and integration of vital information including highly selective biometrics characteristic possessed by any of the twins. In the multimodal biometrics twins identification domain, exemplification of the best features from multiple traits of twins and biometrics fusion process remain to be completely resolved. This research attempts to design a new scheme and more effective multimodal biometrics twins identification by introducing the Dis-Eigen feature-based fusion with the capacity in generating a uni-representation and distinctive features of numerous modalities of twins. First, Aspect United Moment Invariant (AUMI) was used as global feature in the extraction of features obtained from the twins handwritingfingerprint shape and style. Then, the feature-based fusion was examined in terms of its generalization. Next, to achieve better classification accuracy, the Dis-Eigen feature-based fusion algorithm was used. A total of eight distinctive classifiers were used in executing four different training and testing of environment settings. Accordingly, the most salient features of Dis-Eigen feature-based fusion were trained and tested to determine the accuracy of the classification, particularly in terms of performance. The results show that the identification of twins improved as the error of similarity for intra-class decreased while at the same time, the error of similarity for inter-class increased. Hence, with the application of diverse classifiers, the identification rate was improved reaching more than 93%. It can be concluded from the experimental outcomes that the proposed method using Receiver Operation Characteristics (ROC) considerably increases the twins handwriting-fingerprint identification process with 90.25% rate of identification when False Acceptance Rate (FAR) is at 0.01%. It is also indicated that 93.15% identification rate is achieved when FAR is at 0.5% and 98.69% when FAR is at 1.00%. The new proposed solution gives a promising alternative to twins identification application

Global features with identical twins biometric identification system

Studies in pattern recognition domain currently revolve around twin’s biometric identification. The twins’ biometric Identification system may lead to the discovery of a distinguishing pattern of a biometric of an individual. A significant improvement can also be seen in the Unimodal biometric identification; it allows accurate and reliable identification of identical twins with good performance of certain traits. However, since the similarity level is very high, Identical twins’ identification is much more difficult when compared to that of non-twins. Hence, the use of more than one biometric trait with global features is proposed. Further, pattern recognition requires the extraction and selection of meaningful features, which leads to the key issue in the identification of twin handwriting-fingerprint, that is, the question of how to acquire features from many writing and styles twin handwriting-fingerprint to enable the reflection of the right person between twins. This study thus proposes the global with Aspect United Moment Invariant for global feature extractions with the application of identical twin multi-biometric identification with Inter-class and Intra-class

Biometric identification using global discretezation

Biometrics is the science and technology that involves the measurement and analysis of the human body’s biological data. Biometrics involves the extraction a feature set from the obtained data. The feature set is then compared against the template set stored the database. Identification of people must demonstrate reliability and accurately especially in the domains of business transactions and in the access to confidential information. The currently available fingerprint biometric Identification concentrates on feature extraction and task of classification for authorship identification. In fingerprint, the random representation may cause degradation to the performance of classification. Thus, prior to the classification task, certain standards should be present to denote these unique features. In relation to this, the application of the discretization technique would be beneficial. Hence, a new framework for fingerprint biometric identification is proposed. This paper particularly shows the outcome of discretization process on fingerprint samples to attain individual identification. In this paper, the new proposed framework and classic framework were compared using samples. Based on the results, classification accuracies of 90% were obtained when using discretization process with fingerprint biometric identification

Twins multimodal biometric identification system with aspect united moment invariant

In the field of pattern recognition twin’s biometric identification is currently a popularly studied subject. In some situations, the mechanism of twins’ biometric Identification leads to the finding a distinctive pattern of a person’s biometric. Correspondingly, there has been considerable improvement made on the Unimodal biometric identification to identify identical twins with respect to its accuracy and reliability, with some traits that show sound performance. However, owing to great level of similarity, it is much more challenging to identify Identical twins as opposed to identifying non-twins. In order to deal with this problem, the application of more than one biometric trait is proposed; the Multimodal biometric system. Meanwhile, in pattern recognition it is crucial to extract and select features that are meaningful. This brings the attention to the major issue in twin handwriting-fingerprint identification: how to obtain features from numerous writing and styles twin handwriting-fingerprint so that the right person between twins can be reflected. Hence, the Aspect United Moment Invariant is proposed in this study as extraction of feature with identical twin multi-biometric identification

New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance

Human action recognition is an active research area in computer vision because of its immense application in the field of video surveillance, video retrieval, security systems, video indexing and human computer interaction. Action recognition is classified as the time varying feature data generated by human under different viewpoint that aims to build mapping between dynamic image information and semantic understanding. Although a great deal of progress has been made in recognition of human actions during last two decades, few proposed approaches in literature are reported. This leads to a need for much research works to be conducted in addressing on going challenges leading to developing more efficient approaches to solve human action recognition. Feature extraction is the main tasks in action recognition that represents the core of any action recognition procedure. The process of feature extraction involves transforming the input data that describe the shape of a segmented silhouette of a moving person into the set of represented features of action poses. In video surveillance, global moment invariant based on Geometrical Moment Invariant (GMI) is widely used in human action recognition. However, there are many drawbacks of GMI such that it lack of granular interpretation of the invariants relative to the shape. Consequently, the representation of features has not been standardized. Hence, this study proposes a new scheme of human action recognition (HAR) with geometrical moment invariants for feature extraction and supervised invariant discretization in identifying actions uniqueness in video sequencing. The proposed scheme is tested using IXMAS dataset in video sequence that has non rigid nature of human poses that resulting from drastic illumination changes, changing in pose and erratic motion patterns. The invarianceness of the proposed scheme is validated based on the intra-class and inter-class analysis. The result of the proposed scheme yields better performance in action recognition compared to the conventional scheme with an average of more than 99% accuracy while preserving the shape of the human actions in video images

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio