Integrated Data System Person Identification: Accuracy Requirements and Methods

2012 ◽  
Author(s):  
Ting Zhang ◽  
David W. Stevens
Keyword(s):  
Data System ◽  
Identification Accuracy ◽  
Person Identification

RF-Identity

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Chao Feng ◽  
Jie Xiong ◽  
Liqiong Chang ◽  
Fuwei Wang ◽  
Ju Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Critical Role ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Frequency Bandwidth ◽  
Person Identification ◽  
Data Set ◽  
Rf Devices ◽  
Large Frequency

Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.

scholarly journals Eyewitness Memory for Person Identification: Predicting Mugbook Recognition Accuracy According to Person Description Abilities and Subjective Confidence of Witnesses

2021 ◽  
Vol 12 ◽  
Author(s):  
Alexander Handler ◽  
Sascha Frühholz
Keyword(s):  
Recognition Accuracy ◽  
Identification Accuracy ◽  
Subjective Rating ◽  
Facial Features ◽  
Person Identification ◽  
Actual Performance ◽  
Performance Accuracy ◽  
Person Description ◽  
Subjective Estimation ◽  
Subjective Confidence

Mugbook searches are conducted in case a suspect is not known and to assess if a previously convicted person might be recognized as a potential culprit. The goal of the two experiments reported here was to analyze if prior statements and information about the suspect can aid in the evaluation if such a mugbook search is subsequently advised or not. In experiment 1, memory accuracy for person descriptors was tested in order to analyze, which attributes could be chosen to down-scale the mugbook prior to testing. Results showed that age was the most accurate descriptor, followed by ethnicity and height. At the same time self-assessed low subjective accuracy of culprit descriptions by the witness seemed to be divergent to the objective actual performance accuracy. In experiment 2, a mugbook search was conducted after participants viewed a video of a staged crime and gave a description of the culprit. Results showed that accuracy in mugbook searches correlated positively with the total number of person descriptors given by the witness as well as with witness’ description of external facial features. Predictive confidence (i.e., subjective rating of own performance in the subsequent mugbook search), however did not show any relation to the identification accuracy in the actual mugbook search. These results highlight the notion that mugbooks should not be conducted according to the subjective estimation of the witness’ performance but more according to the actual statements and descriptions that the witness can give about the culprit.

Person Identification Using Top-View Image with Depth Information

2013 ◽  
Vol 1 (2) ◽  
pp. 67-79
Author(s):  
Daichi Kouno ◽  
Kazutaka Shimada ◽  
Tsutomu Endo
Keyword(s):  
Body Height ◽  
High Accuracy ◽  
Identification Accuracy ◽  
Depth Information ◽  
Identification Task ◽  
Person Identification ◽  
Identification Methods ◽  
Rich Information ◽  
Accuracy Rates ◽  
Top View

In this paper, the authors describe a novel image-based person identification task. Traditional face-based person identification methods have a low tolerance for occluded situation, such as overlapping of people in an image. The authors focus on an image from an overhead camera. The authors utilize depth information for the identification task. By using depth information, the authors can capture the precise person’s area and rich information for the identification task as compared with popular RGB cameras. The authors apply four features extracted from images based on depth information to the identification method; (1) estimated body height, (2) estimated body dimensions, (3) estimated body size and (4) depth histogram. In the experiment, the authors evaluated two situations; (a) standing in front of a door and (b) touching a doorknob. The identification accuracy rates are 94.4% and 91.4% on the two situations. The authors obtained the high accuracy by the proposed method.

scholarly journals Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

2018 ◽  
Vol 28 (06) ◽  
pp. 1750064 ◽  
Author(s):  
Vitaly Schetinin ◽  
Livija Jakaite ◽  
Ndifreke Nyah ◽  
Dusica Novakovic ◽  
Wojtek Krzanowski
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Identification Accuracy ◽  
Group Method ◽  
Autoregressive Modeling ◽  
Eeg Signals ◽  
Person Identification ◽  
Benchmark Data ◽  
Eeg Data ◽  
The Brain

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

Perceptual Evaluation of Speech Naturalness in Speakers of Varying Gender Identities

2020 ◽  
Vol 63 (7) ◽  
pp. 2054-2069
Author(s):  
Brandon Merritt ◽  
Tessa Bent
Keyword(s):  
Spontaneous Speech ◽  
Identification Accuracy ◽  
Rating Task ◽  
Gender Identification ◽  
Identification Task ◽  
Male And Female ◽  
Perceptual Evaluation ◽  
Speech Training ◽  
And Gender ◽  
Speech Naturalness

Purpose The purpose of this study was to investigate how speech naturalness relates to masculinity–femininity and gender identification (accuracy and reaction time) for cisgender male and female speakers as well as transmasculine and transfeminine speakers. Method Stimuli included spontaneous speech samples from 20 speakers who are transgender (10 transmasculine and 10 transfeminine) and 20 speakers who are cisgender (10 male and 10 female). Fifty-two listeners completed three tasks: a two-alternative forced-choice gender identification task, a speech naturalness rating task, and a masculinity/femininity rating task. Results Transfeminine and transmasculine speakers were rated as significantly less natural sounding than cisgender speakers. Speakers rated as less natural took longer to identify and were identified less accurately in the gender identification task; furthermore, they were rated as less prototypically masculine/feminine. Conclusions Perceptual speech naturalness for both transfeminine and transmasculine speakers is strongly associated with gender cues in spontaneous speech. Training to align a speaker's voice with their gender identity may concurrently improve perceptual speech naturalness. Supplemental Material https://doi.org/10.23641/asha.12543158

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