A New Face Detection Framework Based on Adaptive Cascaded Network

2020 ◽  
pp. 233-242
Author(s):  
Gangting Liu ◽  
Jianhong Lin ◽  
Yifan Ding ◽  
Shihong Yang ◽  
Yunhua Xu
Keyword(s):  
Face Detection

Face Detection for AIBO Using RBF Network

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

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

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.

Efficient Face Detection on Epiphany Multicore Processor

2017 ◽  
Vol 1 (1) ◽  
pp. 113-127 ◽  
Author(s):  
A. A. Sukhinov ◽  
◽  
G. B. Ostrobrod ◽  
Keyword(s):  
Face Detection ◽  
Multicore Processor

Recognizing Human-Object Using Genetic Algorithm for Face Detection in Natural Driving Environment

2012 ◽  
Vol 7 (2) ◽  
pp. 10-18
Author(s):  
B. Mallikarjuna ◽  
◽  
K.V. Ramanaiah ◽  
P. Mohanaiah ◽  
V. Vijaya Kumar Reddy ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Face Detection ◽  
Human Object

Real AdaBoost face detection method based on multi-threshold

2009 ◽  
Vol 29 (8) ◽  
pp. 2098-2100
Author(s):  
Shi-ming SUN ◽  
Qing PAN ◽  
You-fang JI
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Real Adaboost

Rotation Invariant Neural Network-Based Face Detection

1997 ◽  
Author(s):  
Henry A. Rowley ◽  
Shumeet Baluja ◽  
Takeo Kanade
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Rotation Invariant

Video Face Detection Using Bayesian Technique

2019 ◽  
Author(s):  
Seshaiah Merikapudi ◽  
Shrishail Math
Keyword(s):  
Face Detection

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

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