Face Detection from Greyscale Images Using Details from Categorized Wavelet Coefficients as Features for a Dynamic Counterpropagation Network

2007 ◽  
Author(s):  
Yeong Lee Seng ◽  
Li-Minn Ang ◽  
Kah Phooi Seng
Keyword(s):  
Face Detection ◽  
Wavelet Coefficients ◽  
Counterpropagation Network

FACE DETECTION FROM GREYSCALE IMAGES USING DETAILS FROM CATEGORIZED WAVELET COEFFICIENTS AS FEATURES FOR A DYNAMIC SUPERVISED FORWARD PROPAGATION NETWORK

2009 ◽  
Vol 23 (01) ◽  
pp. 3-15 ◽  
Author(s):  
LEE SENG YEONG ◽  
LI-MINN ANG ◽  
KING HANN LIM ◽  
KAH PHOOI SENG
Keyword(s):  
Face Detection ◽  
Detection Rate ◽  
Image Data ◽  
False Positives ◽  
Experimental Results ◽  
Training Data ◽  
Correct Detection ◽  
Wavelet Coefficients ◽  
Propagation Network ◽  
Counterpropagation Network

A dynamic counterpropagation network based on the forward only counterpropagation network (CPN) is applied as the classifier for face detection. The network, called the dynamic supervised forward-propagation network (DSFPN) trains using a supervised algorithm that grows dynamically during training allowing subclasses in the training data to be learnt. The network is trained using a reduced dimensionality categorized wavelet coefficients of the image data. Experimental results obtained show that a 94% correct detection rate can be achieved with less than 6% false positives.

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
Sign in / Sign up

Export Citation Format

Share Document