Face detection with a 3D model

Fused deposition modelling (FDM) is a process of joining materials based on material entrusion technique to produce objects from 3D model using layer-by-layer technique as opposed to subtractive manufacturing. However, many challenges arise in the FDM-printed part such as warping, first layer problem and elephant food that was led to an error in dimensional accuracy of the printed parts especially for the overhanging parts. Hence, in order to investigate the manufacturability of the FDM printed part, various geometrical and manufacturing features were developed using the benchmarking artifacts. Therefore, in this study, new benchmarking artifacts containing multiple overhang lengths were proposed. After the benchmarking artifacts were developed, each of the features were inspected using 3D laser scanner to measure the dimensional accuracy and tolerances. Based on 3D scanned parts, 80% of the fabricated parts were fabricated within ±0.5 mm of dimensional accuracy as compared with the CAD data. In addition, the multiple overhang lengths were also successfully fabricated with a very significant of filament sagging observed.

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Face Detection for AIBO Using RBF Network

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.130.2031 ◽

2010 ◽

Vol 130 (11) ◽

pp. 2031-2038

Author(s):

Kohki Abiko ◽

Hironobu Fukai ◽

Yasue Mitsukura ◽

Minoru Fukumi ◽

Masahiro Tanaka

Keyword(s):

Face Detection ◽

Rbf Network

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A 3D Model of the Unconventional Play in the Goldwyer Formation: An Integrated Shale Rock Characterisation over the Broome Platform, Canning Basin.

Proceedings of the SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference ◽

10.15530/ap-urtec-2019-198312 ◽

2019 ◽

Author(s):

Lukman Johnson ◽

Gregory Smith ◽

Reza Rezaee ◽

Ali Kadkhodaie

Keyword(s):

3D Model ◽

Canning Basin ◽

Shale Rock

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3D MODEL OF THE ESTABLISHMENT OF A PLASMA ARC BETWEEN TWO AERIAL METALLIC ELECTRODES

High Temperature Material Processes An International Quarterly of High-Technology Plasma Processes ◽

10.1615/hightempmatproc.v7.i1.100 ◽

2003 ◽

Vol 7 (1) ◽

pp. 65-70

Author(s):

B. Barthelemy ◽

C. Girold ◽

C. Delalondre ◽

B. Paya ◽

J. M. Baronnet

Keyword(s):

3D Model ◽

Plasma Arc ◽

Metallic Electrodes

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Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.4.040404 ◽

2020 ◽

Vol 64 (4) ◽

pp. 40404-1-40404-16

Author(s):

I.-J. Ding ◽

C.-M. Ruan

Keyword(s):

Face Detection ◽

Speaker Recognition ◽

Visual Information ◽

Classification Tree ◽

Gaussian Mixture ◽

Recognition Method ◽

Indoor Space ◽

Identity Recognition ◽

Visual Identity ◽

Speaker Classification

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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