Novel algorithm for automatic detection of faces and facial features in color images

2001 ◽  
Author(s):  
Yanchao Xing ◽  
Zheng Tan ◽  
Shuanhu Wu
Keyword(s):  
Automatic Detection ◽  
Color Images ◽  
Facial Features ◽  
Novel Algorithm

scholarly journals From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising

2020 ◽  
Author(s):  
Manfred Hartbauer
Keyword(s):  
Image Denoising ◽  
Color Images ◽  
Insect Vision ◽  
Night Vision ◽  
Primitive Type ◽  
Noise Estimate ◽  
Local Means ◽  
Static Images ◽  
Image Details ◽  
Novel Algorithm

Night active insects inspired the development of image enhancement methods that uncover the information contained in dim images or movies. Here, I describe a novel bionic night vision (NV) algorithm that operates in the spatial domain to remove noise from static images. The parameters of this NV algorithm can be automatically derived from global image statistics and a primitive type of noise estimate. In a first step, luminance values were ln-transformed, and then adaptive local means’ calculations were executed to remove the remaining noise without degrading fine image details and object contours. Its performance is comparable with several popular denoising methods and can be applied to grey-scale and color images. This novel algorithm can be executed in parallel at the level of pixels on programmable hardware.

scholarly journals Automatic detection of bunches of grapes in natural environment from color images

2012 ◽  
Vol 10 (4) ◽  
pp. 285-290 ◽  
Author(s):  
M.J.C.S. Reis ◽  
R. Morais ◽  
E. Peres ◽  
C. Pereira ◽  
O. Contente ◽  
...  
Keyword(s):  
Natural Environment ◽  
Automatic Detection ◽  
Color Images

scholarly journals Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yue Liu ◽  
Yibing Li ◽  
Hong Xie ◽  
Dandan Liu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Facial Features ◽  
Fisher Criterion ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Face Images ◽  
Kernel Fisher Discriminant Analysis ◽  
Multiple Data ◽  
Novel Algorithm

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm.

USING WEIGHTED FIXED NEURAL NETWORKS FOR UNSUPERVISED FUZZY CLUSTERING

2002 ◽  
Vol 12 (06) ◽  
pp. 425-434 ◽  
Author(s):  
HAMED HAMID MUHAMMED
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Clustering ◽  
Input Data ◽  
Color Images ◽  
Computationally Efficient ◽  
Data Set ◽  
Local Densities ◽  
Unsupervised Fuzzy Clustering ◽  
Novel Algorithm

A novel algorithm for unsupervised fuzzy clustering is introduced. The algorithm uses a so-called Weighted Fixed Neural Network (WFNN) to store important and useful information about the topological relations in a given data set. The algorithm produces a weighted connected net, of weighted nodes connected by weighted edges, which reflects and preserves the topology of the input data set. The weights of the nodes and the edges in the resulting net are proportional to the local densities of data samples in input space. The connectedness of the net can be changed, and the higher the connectedness of the net is chosen, the fuzzier the system becomes. The new algorithm is computationally efficient when compared to other existing methods for clustering multi-dimensional data, such as color images.

Diagonal Maximum Scatter Difference Analysis and its Application to Face Recognition

2010 ◽  
Vol 20-23 ◽  
pp. 123-128
Author(s):  
Jian Guo Wang ◽  
Tie Jun Zhang
Keyword(s):  
Face Recognition ◽  
Facial Features ◽  
Dimensional Vector ◽  
Human Face ◽  
Human Face Recognition ◽  
Difference Analysis ◽  
One Dimensional ◽  
Face Images ◽  
The One ◽  
Novel Algorithm

Diagonal maximum scatter difference (DiaMSD) method for face recognition is proposed in this paper. This novel algorithm is developed based on two techniques, i.e., maximum scatter difference (MSD) and diagonal face images based projection. The DiaMSD method is not only computationally more efficient but also more accurate than the one dimensional (vector-based) MSD method in extracting the facial features for human face recognition. Extensive experiments are performed to test and evaluate the new algorithm using a subset of the FERET face databases. Experimental results show the effectiveness of the proposed method (DiaMSD).

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