scholarly journals Person Recognition Using Soft Biometric

2021 ◽  
Author(s):  
Lavanya Sriniva
Keyword(s):  
Video Surveillance ◽  
False Positive ◽  
Intelligent Systems ◽  
Gait Recognition ◽  
Semantic Features ◽  
Global Security ◽  
Person Recognition ◽  
False Positive Detection ◽  
Security Concerns

Abstract Global security concerns raised the multiplication of video surveillance devices. Intelligent systems identify the person captured at the different cameras, angles, views, background, and wearing different accessories. Gait is measured at a distance without human cooperation. In this work, gait recognition is increased by concatenating semantic features with traditional features. Combining features are dimensionally reduced and classified using classifiers. This method motivates in future to increase the gait recognition with less false positive detection.

Investigation and Resolution of Interference in the LC-QTOF-MS Detection of 4-MePPP

2021 ◽  
Author(s):  
Darren R Allen ◽  
Christopher Warnholtz ◽  
Brett C McWhinney
Keyword(s):  
Reference Material ◽  
Spectral Data ◽  
False Positive ◽  
In Silico ◽  
Spectral Library ◽  
In Silico Fragmentation ◽  
False Positive Detection ◽  
Product Ions ◽  
Synthetic Cathinone ◽  
Recent Addition

Abstract An interference resulting in the false-positive detection of the synthetic cathinone 4-MePPP in urine was suspected following the recent addition of 4-MePPP spectral data to an LC-QTOF-MS drug library. Although positive detection criteria were achieved, it was noted that all urine samples suspected of containing 4-MePPP also concurrently contained high levels of tramadol and its associated metabolites. Using QTOF-MS software elucidation tools, candidate compounds for the suspected interference were proposed. To provide further confidence in the identity of the interference, in silico fragmentation tools were used to match product ions generated in the analysis with product ions predicted from the theoretical fragmentation of candidate compounds. The ability of the suspected interference to subsequently produce the required product ions for spectral library identification of 4-MePPP was also tested. This information was used to provide a high preliminary confidence in the compound identity prior to purchase and subsequent confirmation with certified reference material. A co-eluting isobaric interference was identified and confirmed as an in-source fragment of the tramadol metabolite, N,N-bisdesmethyltramadol. Proposed resolutions for this interference are also described and subsequently validated by retrospective interrogation of previous cases of suspected interference.

scholarly journals False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1167 ◽  
Author(s):  
Yeunghak Lee ◽  
Jaechang Shim
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Learning Algorithm ◽  
Detection Performance ◽  
Economic Losses ◽  
Smoke Detection ◽  
Temporal Features ◽  
False Positive Detection ◽  
Surveillance Cameras ◽  
Fire Flame

Fire must be extinguished early, as it leads to economic losses and losses of precious lives. Vision-based methods have many difficulties in algorithm research due to the atypical nature fire flame and smoke. In this study, we introduce a novel smoke detection algorithm that reduces false positive detection using spatial and temporal features based on deep learning from factory installed surveillance cameras. First, we calculated the global frame similarity and mean square error (MSE) to detect the moving of fire flame and smoke from input surveillance cameras. Second, we extracted the fire flame and smoke candidate area using the deep learning algorithm (Faster Region-based Convolutional Network (R-CNN)). Third, the final fire flame and smoke area was decided by local spatial and temporal information: frame difference, color, similarity, wavelet transform, coefficient of variation, and MSE. This research proposed a new algorithm using global and local frame features, which is well presented object information to reduce false positive based on the deep learning method. Experimental results show that the false positive detection of the proposed algorithm was reduced to about 99.9% in maintaining the smoke and fire detection performance. It was confirmed that the proposed method has excellent false detection performance.

scholarly journals Accounting for heterogeneity in false‐positive detection rate in southeastern beach mouse habitat occupancy models

Ecosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
Author(s):  
Eric D. Stolen ◽  
Donna M. Oddy ◽  
Shanon L. Gann ◽  
Karen G. Holloway‐Adkins ◽  
Stephanie A. Legare ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Rate ◽  
Occupancy Models ◽  
Habitat Occupancy ◽  
Positive Detection Rate ◽  
False Positive Detection ◽  
False Positive Detection Rate

Elucidation of False Detection of Pesticides During Residue Analysis in Indian Tobacco by Multidimensional GC-MS

2020 ◽  
Vol 103 (1) ◽  
pp. 55-61
Author(s):  
Rakesh Kumar Ghosh ◽  
Zareen S Khan ◽  
Kaushik Banerjee ◽  
D Damodar Reddy ◽  
Nalli Johnson ◽  
...  
Keyword(s):  
Retention Time ◽  
False Positive ◽  
Residue Analysis ◽  
Aroma Compound ◽  
Analysis Tool ◽  
False Detection ◽  
Tobacco Leaf ◽  
Gc Analysis ◽  
Consumer Safety ◽  
False Positive Detection

Abstract Background: False detection of pesticides in agricultural produce may raise serious questions regarding both consumer safety and trade. High levels of delta-hexachlorocyclohexane (δ-HCH; 11.7–22.3 mg/kg) were detected in some tobacco samples in a retention time–based GC analysis. Hence, the selection of an appropriate analytical method is an uncompromisable necessity. Objectives: This research work aimed to elucidate false detection of pesticides along with identification of coeluting tobacco matrix compounds to understand the dynamics of false detection with an increase in the number of analyzed pesticides and to screen suitable analytical methods. Methods: Initially, retention time–based GC analysis was performed for monitoring of 10 pesticide residues in tobacco leaf matrix, followed by GC-MS/selected-ion monitoring (SIM) analysis. Then, the total number of pesticides to be analyzed was increased to 47, and residue analysis was performed by involving GC-MS/SIM and multidimensional (MD) GC-MS. Results: A false-positive detection of δ-HCH due to a coeluting tobacco aroma compound, neophytadiene, during residue analysis of 10 pesticides in tobacco (Nicotiana tabacum L.) leaf was observed. This problem was resolved by employing the unique quantifier and qualifier ions in SIM mode. However, with 47 pesticides, neophytadiene completely masked the signal of δ-HCH, which resulted in an impure spectrum of δ-HCH (<30% similarity match) even after application of selective quantifier and qualifier ions. Finally, MDGC-MS analysis could resolve it by chromatographic separation of the said analyte from the coeluting matrix compound. Conclusions: The findings of this work offer the potential to minimize false reporting of target pesticides to comply with consumer safety and trade standards. Highlights: The study identifies various tobacco matrix compounds coeluting with pesticides during multiresidue analysis. Neophytadiene, a tobacco aroma compound, resulted in false-positive detection of δ-HCH. The MDGC-MS could be effectively used as a confirmatory analysis tool for reliable detection of pesticide residue in tobacco leaf matrix.

scholarly journals Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking

2015 ◽  
Vol 26 (22) ◽  
pp. 4057-4062 ◽  
Author(s):  
Carlas S. Smith ◽  
Sjoerd Stallinga ◽  
Keith A. Lidke ◽  
Bernd Rieger ◽  
David Grunwald
Keyword(s):  
Single Molecule ◽  
False Positive ◽  
Cell Biology ◽  
Expert Knowledge ◽  
Detection Efficiency ◽  
Hypothesis Test ◽  
Yeast Cells ◽  
Particle Detection ◽  
False Positive Detection

Single-molecule detection in fluorescence nanoscopy has become a powerful tool in cell biology but can present vexing issues in image analysis, such as limited signal, unspecific background, empirically set thresholds, image filtering, and false-positive detection limiting overall detection efficiency. Here we present a framework in which expert knowledge and parameter tweaking are replaced with a probability-based hypothesis test. Our method delivers robust and threshold-free signal detection with a defined error estimate and improved detection of weaker signals. The probability value has consequences for downstream data analysis, such as weighing a series of detections and corresponding probabilities, Bayesian propagation of probability, or defining metrics in tracking applications. We show that the method outperforms all current approaches, yielding a detection efficiency of >70% and a false-positive detection rate of <5% under conditions down to 17 photons/pixel background and 180 photons/molecule signal, which is beneficial for any kind of photon-limited application. Examples include limited brightness and photostability, phototoxicity in live-cell single-molecule imaging, and use of new labels for nanoscopy. We present simulations, experimental data, and tracking of low-signal mRNAs in yeast cells.

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