• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.15-18
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• 2007

Identification of humans from multiple view points is an important task for surveillance and security purposes. For optimal performance the system should use the maximum information available from sensors. Multimodal biometric systems are capable of utilizing more than one physiological or behavioral characteristic for enrollment, verification, or identification. Since gait alone is not yet established as a very distinctive feature, this paper presents an approach to fuse face and gait for identification. In this paper we will use the single camera case i.e. both the face and gait recognition is done using the same set of images captured by a single camera. The aim of this paper is to improve the performance of the system by utilizing the maximum amount of information available in the images. Fusion is considered at decision level. The proposed algorithm is tested on the NLPR database.

• Safety and Health at Work
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• v.6 no.2
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• pp.104-113
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• 2015

Background: Given the long hours on the road involving multiple and interacting work stressors (i.e., delivery pressures, irregular shifts, ergonomic hazards), commercial drivers face a plethora of health and safety risks. Researchers goal was to determine whether and to what extent long-haul trucker work schedules influence sleep duration and quality. Methods: Survey and biometric data collected from male long-haul truck drivers at a major truckstop in central North Carolina over a six month period. Results: Daily hours worked (mean = 11 hours, 55 minutes) and frequency of working over government-mandated daily HOS regulations (23.8% "frequently or always") were statistically significant predictors of sleep duration. Miles driven per week (mean = 2,812.61), irregular daily hours worked (63.8%), and frequency of working over the daily hour limit (23.8% "frequently or always") were statistically significant predictors of sleep quality. Conclusion: Implications of findings suggest a comprehensive review of the regulations and operational conditions for commercial motor vehicle drivers be undertaken.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.117-137
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• 2010

Iris recognition is a biometric technique which uses unique iris patterns between the pupil and sclera. The advantage of iris recognition lies in high recognition accuracy; however, for good performance, it requires the diameter of the iris to be greater than 200 pixels in an input image. So, a conventional iris system uses a camera with a costly and bulky zoom lens. To overcome this problem, we propose a new method to restore a low resolution iris image into a high resolution image using a single image. This study has three novelties compared to previous works: (i) To obtain a high resolution iris image, we only use a single iris image. This can solve the problems of conventional restoration methods with multiple images, which need considerable processing time for image capturing and registration. (ii) By using bilinear interpolation and a constrained least squares (CLS) filter based on the degradation model, we obtain a high resolution iris image with high recognition performance at fast speed. (iii) We select the optimized parameters of the CLS filter and degradation model according to the zoom factor of the image in terms of recognition accuracy. Experimental results showed that the accuracy of iris recognition was enhanced using the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4076-4092
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• 2019

Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.8_spc
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• pp.1275-1283
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• 2019

Goats (Capra hircus) were domesticated during the late Neolithic, approximately 10,500 years ago, and humans exerted minor selection pressure until fairly recently. Probably the largest genetic change occurring over the millennia happened via natural selection and random genetic drift, the latter causing genes to be fixed in small and isolated populations. Recent human-influenced genetic changes have occurred through biometrics and genomics. For the most part, biometrics has concentrated upon the refining of estimates of heritabilities and genetic correlations. Heritabilities are instrumental in the calculation of estimated breeding values and genetic correlations are necessary in the construction of selection indices that account for changes in multiple traits under selection at one time. Early genomic studies focused upon microsatellite markers, which are short tandem repeats of nucleic acids and which are detected using polymerase chain reaction primers flanking the microsatellite. Microsatellite markers have been very important in parentage verification, which can impact genetic progress. Additionally, microsatellite markers have been a useful tool in assessing genetic diversity between and among breeds, which is important in the conservation of minor breeds. Single nucleotide polymorphisms are a new genomic tool that have refined classical BLUP methodology (biometric) to provide more accurate genomic estimated breeding values, provided a large reference population is available.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1065-1071
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• 2023

In this paper discuss with research developing an LSTM model for classifying the behavior of occupants within a residence. The multi-sensor consists of an IAQ (Indoor Air Quality) sensor that measures indoor air quality, a UWB radar that tracks occupancy detection and location, and a Piezo sensor to measure occupants' biometric information, and collects occupant behavior data such as going out, staying, cooking, cleaning, exercise, and sleep by constructed an experimental environment similar to the actual residential environment. After the data with removed outliers and missing, the LSTM model is used to calculate accuracy, sensitivity, specificity of the occupant behavior classification model, T1 score.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.72-77
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• 2008

Identification of humans from multiple view points is an important task for surveillance and security purposes. For optimal performance the system should use the maximum information available from sensors. Multimodal biometric systems are capable of utilizing more than one physiological or behavioral characteristic for enrollment, verification, or identification. Since gait alone is not yet established as a very distinctive feature, this paper presents an approach to fuse face and gait for identification. In this paper we will use the single camera case i.e both the face and gait recognition is done using the same set of images captured by a single camera. The aim of this paper is to improve the performance of the system by utilizing the maximum amount of information available in the images. Fusion in considered at decision level. The proposed algorithm is tested on the NLPR database.

• International Journal of Industrial Entomology and Biomaterials
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• v.27 no.2
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• pp.243-264
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• 2013

Antheraea mylitta Drury is a commercial silk producing forest insect in India and Xanthopimpla pedator Fabricius is its larval-pupal endoparasitoid, which causes pupal mortality that affects seed production. Effects of host plants, rearing season and their interactions on parasitic behaviour of X. pedator were studied here, as influence of these factors on biological success of X. pedator is not known. Seven forest tree species were tested as food plants for A. mylitta, and rate of pupal parasitization in both the rearing seasons were recorded and analysed. Results showed that rearing season and host plants significantly affected the rate of pupal parasitization in both the sexes. Pupal mortality was found significantly higher (14.52%) in second rearing season than the first (2.89%). Likewise, host plants and rearing seasons significantly affected length, diameter, and shell thickness of cocoons in both sexes. Out of all infested pupae, 85.59% were found male, which indicated that X. pedator chooses male spinning larva of A. mylitta for oviposition, but we could not answer satisfactorily the why and how aspect of this sex specific parasitic behaviour of X. pedator. Multiple regression analysis indicated that length and shell thickness of male cocoons are potential predictors for pupal parasitization rate of X. pedator. Based on highest cocoon productivity and lowest pupal mortality, Terminalia alata, T. tomentosa, and T. arjuna were found to be the most suitable host plants for forest based commercial rearing of A. mylitta in tropical forest areas of Uttarakhand state, where it has never been reared earlier. Sex and season specific interaction of X. pedator with its larval-pupal host, A. mylitta is a novel entomological study to find out explanations for some of the unresolved research questions on parasitic behaviour of X. predator that opens a new area for specialised study on male specific parasitization in Ichneumonidae.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.591-607
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• 2013

The objectives of the study were to evaluate the aquatic environment of an urban stream using various ecological parameters of biological biomarkers, physical habitat quality and chemical water quality and to develop a "Multimetric Eco-Model" ($M_m$-E Model) for the ecosystem evaluations. For the applications of the $M_m$-E model, three zones including the control zone ($C_Z$) of headwaters, transition zone ($T_Z$) of mid-stream and the impacted zone ($I_Z$) of downstream were designated and analyzed the seasonal variations of the model values. The biomarkers of DNA, based on the comet assay approach of single-cell gel electrophoresis (SCGE), were analyzed using the blood samples of Zacco platypus as a target species, and the parameters were used tail moment, tail DNA(%) and tail length (${\mu}m$) in the bioassay. The damages of DNA were evident in the impacted zone, but not in the control zone. The condition factor ($C_F$) as key indicators of the population evaluation indicator was analyzed along with the weight-length relation and individual abnormality. The four metrics of Qualitative Habitat Evaluation Index (QHEI) were added for the evaluations of physical habitat. In addition, the parameters of chemical water quality were used as eutrophic indicators of nitrogen (N) and phosphorus (P), chemical oxygen demand (COD) and conductivity. Overall, our results suggested that attributes of biomarkers and bioindicators in the impacted zone ($I_Z$) had sensitive response largely to the chemical stress (eutrophic indicators) and also partially to physical habitat quality, compared to the those in the control zone.