Face detection in color and infrared images

Various applications using a camera system have been developed and deployed commercially to improve our daily life. The performance of camera system is mainly dependent on image quality and illumination conditions. Multiband camera has been developed to provide a wealth of information for image acquisition. In this paper, we developed two applications about image segmentation and face detection using a multiband camera, which is available in four bands consisting of a near infrared and three color bands. We proposed a multiband camera system to utilize two different images i.e. color image extracted from Bayer filter and near infrared images. The experimental results showed the effectiveness of the proposed system.

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Face detection in color and infrared images

Face Detection and Recognition ◽

10.1201/b19349-6 ◽

2015 ◽

pp. 83-105

Keyword(s):

Face Detection ◽

Infrared Images

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Time-to-go estimation from infrared images

IEE Proceedings I Communications Speech and Vision ◽

10.1049/ip-i-2.1992.0049 ◽

1992 ◽

Vol 139 (3) ◽

pp. 356

Author(s):

K.C. Markham

Keyword(s):

Infrared Images

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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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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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Efficient Face Detection on Epiphany Multicore Processor

COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES ◽

10.23947/2587-8999-2017-1-1-113-127 ◽

2017 ◽

Vol 1 (1) ◽

pp. 113-127 ◽

Cited By ~ 2

Author(s):

A. A. Sukhinov ◽

◽

G. B. Ostrobrod ◽

Keyword(s):

Face Detection ◽

Multicore Processor

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Integrated Approach for Sclera Recognition and Eye Gaze Detection

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i7.188 ◽

2018 ◽

Vol 6 (7) ◽

pp. 77

Author(s):

Snehal S. Rajole ◽

J. V. Shinde

Keyword(s):

Near Infrared ◽

Eye Gaze ◽

Integrated Approach ◽

Error Rates ◽

Infrared Images ◽

Scaling Method ◽

Extensive Simulation ◽

Sclera Recognition ◽

Gaze Detection ◽

Biometric Trait

In this paper we proposed unique technique which is adaptive to noisy images for eye gaze detection as processing noisy sclera images captured at-a-distance and on-the-move has not been extensively investigated. Sclera blood vessels have been investigated recently as an efficient biometric trait. Capturing part of the eye with a normal camera using visible-wavelength images rather than near infrared images has provoked research interest. This technique involves sclera template rotation alignment and a distance scaling method to minimize the error rates when noisy eye images are captured at-a-distance and on-the move. The proposed system is tested and results are generated by extensive simulation in java.

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