Properties of personal identification systems using question‐answer technique

A sole biometric identifier in construction an individual identification is often not able to meet the preferred performance requirements. Biometric identification pedestal on multiple biometrics correspond to an up-and-coming trend. computerized biometric scheme for person identification gauge a “signature” of the human body, compare the resulting characteristic to a database, and cause to be an request dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are classically characteristic, even though point in time varying, such as Face recognition, Iris recognition, Fingerprint verification, Palm print verification in making a personal identification. Multi-biometric systems, which consolidate in sequence from multiple biometric sources, are in advance popularity since they are able to conquer limitations such as non-universality, noisy sensor data, large intra-user difference and vulnerability to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the concept issues and the applications strategies of multi-biometric systems.

This paper presents an intelligent system for person identification with biometric measures such as signature, fingerprint and face. We describe the neural network architectures used to achieve person identification based on the biometrics measures. Simulation results show that the proposed method provides good recognition.

Three experiments were conducted to find out whether the standard Implicit Association Test (IAT) could be used to distinguish truthful and deceitful witnesses. We anticipated that IAT effects would be greater after lying. Participants were asked to answer questions with incorrect answers (i.e., the lie condition) or correct answers (i.e., the truthful condition). A third group of participants were not interviewed (a control group). Participants then took the IAT, in which they were asked to associate correct and incorrect answers with positive or negative attributes. Results demonstrate that standard IAT effects are greater after lying than after truth telling, but only when attribute labels were clearly and explicitly linked to positive and negative affect. Theoretical implications are considered.

High-density event-related potentials were recorded to examine the electrophysiologic correlates of the evaluation of possible answers provided during a Chinese character-generation task. We examined three conditions: the character given was what participants initially generated (Consistent answer), the character given was correct (Unexpected Correct answer), or it was incorrect (Unexpected Incorrect answer). Results showed that Unexpected Correct and Incorrect answers elicited a more negative event-related potential deflection (N320) than did Consistent answers between 300 and 400 ms. Dipole source analysis of difference waves (Unexpected Correct or Incorrect minus Consistent answers) localized the generator of the N320 in the anterior cingulate cortex. The N320 therefore likely reflects the cognitive change or conflict between old and new ways of thinking while identifying and judging characters.

This paper presents the iris recognition system for biometric personal identification using neural network. Personal identification consists of localization of the iris region and generation of a data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The results of simulations illustrate the effectiveness of the neural system in personal identification.

In this paper an improved face detection method based on our previous Information-Based Maximum Discrimination approach is presented that maximizes the discrimination between face and non-face examples in a training set without even using color or motion information. A short review of our previous method is given together with the description of the intuition behind and our recent improvement on its detection speed. A person identification system has been developed that performs multi-modal person identification in real-time video based on this newly improved face detection method together with speaker identification.

This paper investigates the potential of surface electromyography (sEMG) as a new biometric modality and proposes a deep neural network architecture as the backbone of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gesture-independent</i> personal identification system. The paper focuses on the real-world translation of such a model through systematic optimization, which finds the minimum number of gestures and sensors needed for training. Focusing on “dynamic sEMG”, our proposed method can successfully identify 40 subjects with an average accuracy of 97%. This is achieved when gestures are the same in training, validation, and testing (the subjects need to repeat a particular gesture among a set of seven known gestures as a passcode). In a more complex scenario, when training gestures differ from those in validation and testing, our model can achieve an average accuracy of 90%, demonstrating that the proposed model can extract the unique patterns to identify a user regardless of gestures. Taking advantage of Gradient-weighted Class Activation Mapping (Grad-CAM), we explore the attention of the model on segments of the spectrotemporal space of the input signals. Grad-CAM not only sheds light on sEMG-based personal identification by decoding and visualizing the unique user-specific neurophysiological pattern but also generates a two-dimensional spectrotemporal mask used to reduce the model complexity significantly. As a result of the systematic optimization and Grad-CAM analysis, our proposed identification method needs only 4% of data for training, boosting practicality. This paper also reveals the robustness of the proposed model for cross-day evaluation. Finally, the comparative study shows the superiority of our proposed model over several state-of-the-art algorithms.

The heart electrical activity has highly distinctive characteristics for each individual, which motivated us to use the electrocardiogram signal (ECG) as a biometric characteristic. This paper presents a new approach for human recognition from their Electrocardiograms (ECG). The proposed approach is based on an extraction feature with fiducial detection in time domain. It consists of three steps. After the preprocessing step, we extracted ten morphological descriptors and sixty hermite Polynomials Expansion coefficients (HPEc) from each heartbeat which represented the second step. The classification step is done by using a binary SVM and the hidden Markov model (HMM). In this step, a particular strategy of persons' identification was adopted: Firstly, we used four groups of morphological descriptors separately. Secondly, we combined them in one input vector of our classification system. Moreover, we employed the hermite Polynomials Expansion coefficients apart. In order to improve performance, we associated the HPEc with all groups of morphological descriptors as input parameters of our classification system. We tested our algorithm on 18 healthy signals of the MIT_BIH database. The results of experiments showed that the proposed hybrid approach has achieved an identification rate equal to 98.97% using SVM and 99.02% using HMM.

Social games offer a rich and challenging environment to develop and evaluate complex robot behaviors. Social interactions with robots benefit from personalization, which requires person identification—a process that raises privacy concerns. Soft biometrics, however, offer a privacy-preserving alternative by enabling short-term identification without relying on private or sensitive features. In this work, we leverage a zero-shot Visual Question Answering (VQA) person identification system and adapt it to make Pepper play Guess Who? The identification module performance is firstly studied offline in the AveRobot dataset. Afterwards, it is integrated in a ROS based architecture that manages the game flow and enables natural verbal interaction. Experiments conducted with 29 subjects demonstrate that the system achieves identification performance nearly equivalent to that of hard biometric-based systems while also offering engaging and entertaining gameplay. Notably, user feedback reveals a high level of acceptance, highlighting that social gaming with robots is a promising avenue for developing and testing complex behaviors.

A real-time automatic personal identification system should meet the conflicting dual requirements of accuracy and response time. In addition, it also should be user-friendly. We introduce a medium-size realtime automatic personal identification system, F2ID, which integrates faces and fingerprints to make a personal identification. F2ID overcomes some of the limitations of face recognition systems and fingerprint verification systems and can achieve a desirable identification accuracy with a tolerable response time. We have tested our system on a limited set of face and fingerprint images collected in a laboratory environment. Experimental results show that that our system meets both the identification accuracy as well as the speed requirements.

The importance of automatic person identification using non-intrusive biometric modality has created enormous interest in computer vision society over the last few years. For this, gait based person recognition is receiving much more attention in different applications like visual surveillance, security control, people counting. In this paper, we have presented a gait based person identification system using 3D human pose modeling for any arbitrary walking pattern in any unrestricted indoor environment, using Microsoft Kinect sensor. Instead of estimating gait cycle, we have modeled the gait pattern with a spatiotemporal set of key poses and sub-poses which occur periodically in different gait cycles. The robustness of the solution is increased by outlier detection to handle noisy skeleton data obtained from Kinect. The performance of the proposed system is also assessed with rotating Kinect setup to increase the field of view of single Kinect. We have done the average and worst case performance evaluation of the system with respect to the existing Kinect based approaches. It needs to be mentioned that our proposed person identification system is able to achieve a frame level F-score of more than 90% for 20 subjects with fixed Kinect setup.

Why do students give incorrect answers in PISA? What are the reasons for giving incorrect answers? Do all incorrect answers reflect only the lack of competence or might even a competent child make a mistake? The aim of this article is to contribute to a better understanding of these issues. In the current investigation, we selected six students who responded incorrectly to one PISA question in mathematics or science when they solved it individually. Then, we analyzed their understanding of the PISA task and their reasoning about it through a dialogical problem solving in triads to identify why they made an incorrect answer. Moreover, we tried to determine how the shared peer interaction might change the understanding and reasoning of the child and enable her/him to solve the task. The results of this study illustrate the differences between incorrect answers reflecting lack of competence and those incorrect answers, which appear for some other reasons. Based on the dialogical problem solving approach, we analyzed these two types of incorrect answers and the reasoning trajectories behind them.

Reducing admission rates post-stem cell transplant utilizing an educational questionnaire to assess retention of caregiver knowledge

Sensory thresholds are often collected through ascending forced‐choice methods. Group thresholds are important for comparing stimuli or populations; yet, the method has two problems. An individual may correctly guess the correct answer at any concentration step and might detect correctly at low concentrations but become adapted or fatigued at higher concentrations. The survival analysis method deals with both issues. Individual sequences of incorrect and correct answers are adjusted, taking into account the group performance at each concentration. The technique reduces the chance probability where there are consecutive correct answers. Adjusted sequences are submitted to survival analysis to determine group thresholds. The technique was applied to an aroma threshold and a taste threshold study. It resulted in group thresholds similar to ASTM or logarithmic regression procedures. Significant differences in taste thresholds between younger and older adults were determined. The approach provides a more robust technique over previous estimation methods.Practical ApplicationsDetection thresholds can indicate the level at which the average person is likely to notice a taste or smell. Therefore, they serve as an informative benchmark of when a tastant or aroma material will have an effect on consumer perception of that taste or flavor. They also serve as an indicator of the biological (sensory) potency of various taste and flavor materials. The survival analysis method outlined in this paper is recommended for the analysis of threshold data collected through the forced‐choice ascending concentration series method of limits. The new method takes into consideration an individual's sequence of incorrect and correct answers and the group's overall performance for a particular concentration, adjusting the individual's answers in a uniform manner. The adjusted answers can then be submitted to survival analysis statistical estimations to obtain more accurate group thresholds.

In this paper we present a multimodal authentication (person identification) system based on simultaneous recognition of face and speech data using a novel bio-inspired architecture powered by the CM1K chip. The CM1K chip has a constant recognition time irrespective of the size of the knowledge base, which gives massive time gains in learning and recognition over software implementations of similar methods. We demonstrate a system utilizing the CM1K chip as a neural network accelerator along with data pre-processing done by a desktop PC. The system realized consumes energy of the order: 668 &#x220A;J for learning and 487 &#x220A;J for recognition, while operating at 25 MHz. The classification test accuracy of the system is approximately 91%.