Turing’s Menagerie: Talking Lions, Virtual Bats, Electric Sheep and Analogical Peacocks: Common Ground and Common Interest are Necessary Components of Engagement

This theoretical paper attempts to define some of the key components and challenges required to create embodied conversational agents that can be genuinely interesting conversational partners. Wittgenstein’s argument concerning talking lions emphasizes the importance of having a shared common ground as a basis for conversational interactions. Virtual bats suggests that–for some people at least–it is important that there be a feeling of authenticity concerning a subjectively experiencing entity that can convey what it is like to be that entity. Electric sheep reminds us of the importance of empathy in human conversational interaction and that we should provide a full communicative repertoire of both verbal and non-verbal components if we are to create genuinely engaging interactions. Also we may be making the task more difficult rather than easy if we leave out non-verbal aspects of communication. Finally, analogical peacocks highlights the importance of between minds alignment and establishes a longer term goal of being interesting, creative, and humorous if an embodied conversational is to be truly an engaging conversational partner. Some potential directions and solutions to addressing these issues are suggested.

Cascaded multimodal biometric recognition framework

A practically viable multi-biometric recognition system should not only be stable, robust and accurate but should also adhere to real-time processing speed and memory constraints. This study proposes a cascaded classifier-based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users' dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance-based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier-based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.

Investigation into DCT Feature Selection for Visual Lip-Based Biometric Authentication

This paper investigated using lip movements as a behavioural biometric for person authentication. The system was trained, evaluated and tested using the XM2VTS dataset, following the Lausanne Protocol configuration II. Features were selected from the DCT coefficients of the greyscale lip image. This paper investigated the number of DCT coefficients selected, the selection process, and static and dynamic feature combinations. Using a Gaussian Mixture Model - Universal Background Model framework an Equal Error Rate of 2.20% was achieved during evaluation and on an unseen test set a False Acceptance Rate of 1.7% and False Rejection Rate of 3.0% was achieved. This compares favourably with face authentication results on the same dataset whilst not being susceptible to spoofing attacks.

Two-year changes in refractive error and related biometric factors in an adult Chinese population

Importance: This article provides, to our knowledge, the first longitudinal population-based data on refractive error (RE) in Chinese persons.

Objective: To study cohort effects and changes associated with aging in REs among Chinese adults.

Design, Setting, and Participants: A 2-year, longitudinal population-based cohort study was conducted in southern China. Participants, identified using cluster random sampling, included residents of Yuexiu District, Guangzhou, China, aged 35 years or older who had undergone no previous eye surgery.

Methods: Participants underwent noncycloplegic automated refraction and keratometry in December 2008 and December 2010; in a random 50% sample of the participants, anterior segment ocular coherence tomography measurement of lens thickness, as well as measurement of axial length and anterior chamber depth by partial coherence laser interferometry, were performed.

Main Outcomes and Measures: Two-year change in spherical equivalent refraction (RE), lens thickness, axial length, and anterior chamber depth in the right eye.

Results: A total of 745 individuals underwent biometric testing in both 2008 and 2010 (2008 mean [SD] age, 52.2 [11.5] years; 53.7% women). Mean RE showed a 2-year hyperopic shift from −0.44 (2.21) to −0.31 (2.26) diopters (D) (difference, +0.13; 95% CI, 0.11 to 0.16). A consistent 2-year hyperopic shift of 0.09 to 0.22 D was observed among participants aged 35 to 64 years when stratifying by decade, suggesting that a substantial change in RE with aging may occur during this 30-year period. Cross-sectionally, RE increased only in the cohort younger than 50 years (0.11 D/y; 95% CI, 0.06 to 0.16). In the cross-sectional data, axial length decreased at −0.06 mm/y (95% CI, −0.09 to −0.04), although the 2-year change in axial length was positive and thus could not explain the cross-sectional difference. These latter results suggest a cohort effect, with greater myopia developing among younger persons.

Conclusions and Relevance: This first Chinese population-based longitudinal study of RE provides evidence for both important longitudinal aging changes and cohort effects, most notably greater myopia prevalence among younger persons.

Biometric measurements in highly myopic eyes

Purpose: To assess the repeatability and accuracy of optical biometry (Lenstar LS900 optical low-coherence reflectometry [OLCR] and IOLMaster partial coherence interferometry [PCI]) and applanation ultrasound biometry in highly myopic eyes. Setting: Division of Preventive Ophthalmology, Zhongshan Ophthalmic Center, Guangzhou, China. Design: Comparative evaluation of diagnostic technology. Methods: Biometric measurements were taken in highly myopic subjects with a spherical equivalent (SE) of -6.00 diopters (D) or higher and an axial length (AL) longer than 25.0 mm. Measurements of AL and anterior chamber depth (ACD) obtained by OLCR were compared with those obtained by PCI and applanation A-scan ultrasound. Right eyes were analyzed. Repeatability was evaluated using the coefficient of variation (CoV) and agreement, using Bland-Altman analyses. Results: The mean SE was -11.20 D ± 4.65 (SD). The CoVs for repeated AL measurements using OLCR, PCI, and applanation ultrasound were 0.06%, 0.07%, and 0.20%, respectively. The limits of agreement (LoA) for AL were 0.11 mm between OLCR and PCI, 1.01 mm between OLCR and applanation ultrasound, and 1.03 mm between PCI and ultrasound. The ACD values were 0.29 mm, 0.53 mm, and 0.51 mm, respectively. These repeatability and agreement results were comparable in eyes with extreme myopia (AL ≥27.0 mm) or posterior staphyloma. The mean radius of corneal curvature was similar between OLCR and PCI (7.66 ± 0.24 mm versus 7.64 ± 0.25 mm), with an LoA of 0.12 mm. Conclusion: Optical biometry provided more repeatable and precise measurements of biometric parameters, including AL and ACD, than applanation ultrasound biometry in highly myopic eyes. Financial Disclosure: No author has a financial or proprietary interest in any material or method mentioned. © 2012 ASCRS and ESCRS.

A proposed simple method for measurement in the anterior chamber angle: biometric gonioscopy

OBJECTIVE:

To design a system of gonioscopy that will allow greater interobserver reliability and more clearly defined screening cutoffs for angle closure than current systems while being simple to teach and technologically appropriate for use in rural Asia, where the prevalence of angle-closure glaucoma is highest.

DESIGN:

Clinic-based validation and interobserver reliability trial.

PARTICIPANTS:

Study 1: 21 patients 18 years of age and older recruited from a university-based specialty glaucoma clinic; study 2: 32 patients 18 years of age and older recruited from the same clinic.

INTERVENTION:

In study 1, all participants underwent conventional gonioscopy by an experienced observer (GLS) using the Spaeth system and in the same eye also underwent Scheimpflug photography, ultrasonographic measurement of anterior chamber depth and axial length, automatic refraction, and biometric gonioscopy with measurement of the distance from iris insertion to Schwalbe's line using a reticule based in the slit-lamp ocular. In study 2, all participants underwent both conventional gonioscopy and biometric gonioscopy by an experienced gonioscopist (NGC) and a medical student with no previous training in gonioscopy (JK).

MAIN OUTCOME MEASURES:

Study 1: The association between biometric gonioscopy and conventional gonioscopy, Scheimpflug photography, and other factors known to correlate with the configuration of the angle. Study 2: Interobserver agreement using biometric gonioscopy compared to that obtained with conventional gonioscopy.

RESULTS:

In study 1, there was an independent, monotonic, statistically significant relationship between biometric gonioscopy and both Spaeth angle (P = 0.001, t test) and Spaeth insertion (P = 0.008, t test) grades. Biometric gonioscopy correctly identified six of six patients with occludable angles according to Spaeth criteria. Biometric gonioscopic grade was also significantly associated with the anterior chamber angle as measured by Scheimpflug photography (P = 0.005, t test). In study 2, the intraclass correlation coefficient between graders for biometric gonioscopy (0.97) was higher than for Spaeth angle grade (0.72) or Spaeth insertion grade (0.84).

CONCLUSION:

Biometric gonioscopy correlates well with other measures of the anterior chamber angle, shows a higher degree of interobserver reliability than conventional gonioscopy, and can readily be learned by an inexperienced observer.

Biometric gonioscopy and the effects of age, race, and sex on the anterior chamber angle.

AIM:

To utilise a novel method for making measurements in the anterior chamber in order to compare the anterior chamber angles of people of European, African, and east Asian descent aged 40 years and over.

METHODS:

A cross sectional study on 15 people of each sex from each decade from the 40s to the 70s, from each of three racial groups-black, white, and Chinese Singaporeans. Biometric gonioscopy (BG) utilises a slit lamp mounted reticule to make measurements from the apparent iris insertion to Schwalbe's line through a Goldmann one mirror goniolens. The main outcome measures were BG measurements of the anterior chamber angle as detailed above.

RESULTS:

There was no significant difference in angle measurement between black, white, and Chinese races in this study. However, at younger ages people of Chinese race appeared to have deeper angles than white or black people, whereas the angles of older Chinese were significantly narrower (p = 0.004 for the difference in slope of BG by age between Chinese and both black and white people).

CONCLUSION:

The failure to detect a difference in angle measurements between these groups was surprising, given the much higher prevalence of angle closure among Chinese. It appears that the overall apparent similarity of BG means between Chinese and Western populations may mask very different trends with age. The apparently more rapid decline in angle width measurements with age among Chinese may be due to the higher prevalence of cataract or "creeping angle closure." However, longitudinal inferences from cross sectional data are problematic, and this may represent a cohort phenomenon caused by the increasing prevalence of myopia in the younger Singaporean population.

Hybrid authentication systems: towards robust user authentication on wireless and mobile devices using biometric technologies

Adequate user authentication is a persistent problem, particularly with mobile devices, which tend to be highly personal and at the fringes of an organisation's influence. Yet these devices are being used increasingly in various business settings, where they pose a risk to security and privacy, not only from sensitive information they may contain, but also from the means they typically offer to access such information over wireless networks. User authentication is the first line of defence for a mobile device that falls into the hands of an unauthorised user. However, motivating users to enable simple password mechanisms and periodically update their authentication information is difficult at best. This paper examines some of the issues relating to the use of biometrics as a viable method of authentication on mobile wireless devices. It is also a critical analysis of some of the techniques currently employed and where appropriate, suggests novel hybrid ways in which they could be improved or modified. Both biometric technology and wireless setting based constraints that determine the feasibility and the performance of the authentication feature are specified. Some well known biometric technologies are briefly reviewed and their feasibility for wireless and mobile use is reviewed. Furthermore, a number of quantitative and qualitative parameters for evaluation are also presented. Biometric technologies are continuously advancing toward commercial implementation in wireless devices. When carefully designed and implemented, the advantage of biometric authentication arises mainly from increased convenience and coexistent improved security.

Embedded platform for ECG biometric recognition

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Development of a Biometric Personal Authentication System Based on Fingerprint and Speech

Biometrics deals with the physiological and behavioral characteristics of an individual to establish identity. Fingerprint based authentication is the most advanced biometric authentication technology. The minutiae based fingerprint identification method offer reasonable identification rate. The feature minutiae map consists of about 70-100 minutia points and matching accuracy is dropping down while the size of database is growing up. Hence it is inevitable to make the size of the fingerprint feature code to be as smaller as possible so that identification may be much easier. In this research, a novel global singularity based fingerprint representation is proposed. Fingerprint baseline, which is the line between distal and intermediate phalangeal joint line in the fingerprint, is taken as the reference line. A polygon is formed with the singularities and the fingerprint baseline. The feature vectors are the polygonal angle, sides, area, type and the ridge counts in between the singularities. 100% recognition rate is achieved in this method. The method is compared with the conventional minutiae based recognition method in terms of computation time, receiver operator characteristics (ROC) and the feature vector length. Speech is a behavioural biometric modality and can be used for identification of a speaker. In this work, MFCC of text dependant speeches are computed and clustered using k-means algorithm. A backpropagation based Artificial Neural Network is trained to identify the clustered speech code. The performance of the neural network classifier is compared with the VQ based Euclidean minimum classifier. Biometric systems that use a single modality are usually affected by problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multifinger feature level fusion based fingerprint recognition is developed and the performances are measured in terms of the ROC curve. Score level fusion of fingerprint and speech based recognition system is done and 100% accuracy is achieved for a considerable range of matching threshold

A Prototype for a Multimodal Biometric Security system based on Face and Audio Signatures

Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared to individual unimodal recognition systems

Effectiveness Of Feature Detection Operators On The Performance Of Iris Biometric Recognition System

Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.

IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATOR

Biometrics has become important in security applications. In comparison with many other biometric features, iris recognition has very high recognition accuracy because it depends on iris which is located in a place that still stable throughout human life and the probability to find two identical iris's is close to zero. The identification system consists of several stages including segmentation stage which is the most serious and critical one. The current segmentation methods still have limitation in localizing the iris due to circular shape consideration of the pupil. In this research, Daugman method is done to investigate the segmentation techniques. Eyelid detection is another step that has been included in this study as a part of segmentation stage to localize the iris accurately and remove unwanted area that might be included. The obtained iris region is encoded using haar wavelets to construct the iris code, which contains the most discriminating feature in the iris pattern. Hamming distance is used for comparison of iris templates in the recognition stage. The dataset which is used for the study is UBIRIS database. A comparative study of different edge detector operator is performed. It is observed that canny operator is best suited to extract most of the edges to generate the iris code for comparison. Recognition rate of 89% and rejection rate of 95% is achieved

Improved Biometric Authentication System Using Multimodal Cue Integration

Biometrics is an efficient technology with great possibilities in the area of security system development for official and commercial applications. The biometrics has recently become a significant part of any efficient person authentication solution. The advantage of using biometric traits is that they cannot be stolen, shared or even forgotten. The thesis addresses one of the emerging topics in Authentication System, viz., the implementation of Improved Biometric Authentication System using Multimodal Cue Integration, as the operator assisted identification turns out to be tedious, laborious and time consuming. In order to derive the best performance for the authentication system, an appropriate feature selection criteria has been evolved. It has been seen that the selection of too many features lead to the deterioration in the authentication performance and efficiency. In the work reported in this thesis, various judiciously chosen components of the biometric traits and their feature vectors are used for realizing the newly proposed Biometric Authentication System using Multimodal Cue Integration. The feature vectors so generated from the noisy biometric traits is compared with the feature vectors available in the knowledge base and the most matching pattern is identified for the purpose of user authentication. In an attempt to improve the success rate of the Feature Vector based authentication system, the proposed system has been augmented with the user dependent weighted fusion technique.