Competing Alternatives: Science and Math Identity Recognition and Credit Accrual in the Trajectory of STEM Major Plans

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

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Aboriginal service in the First World War: Identity, recognition and the problem of mateship

Aboriginal History Journal ◽

10.22459/ah.39.2015.08 ◽

2015 ◽

Vol 39 ◽

Cited By ~ 4

Author(s):

Philippa Scarlett

Keyword(s):

First World War ◽

World War ◽

Identity Recognition ◽

The First World War ◽

First World

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Feature Extraction for Finger-Vein-Based Identity Recognition

Journal of Imaging ◽

10.3390/jimaging7050089 ◽

2021 ◽

Vol 7 (5) ◽

pp. 89

Author(s):

George K. Sidiropoulos ◽

Polixeni Kiratsa ◽

Petros Chatzipetrou ◽

George A. Papakostas

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Extraction Methods ◽

Identity Recognition ◽

Learning Framework ◽

Finger Vein ◽

The Past ◽

Biometric Systems ◽

Vein Recognition ◽

Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

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