Inverse Constrained Maximum Variance Mapping for Face Recognition

2013 ◽  
Vol 462-463 ◽  
pp. 452-457 ◽  
Author(s):  
Qi Rong Zhang ◽  
Jia Nan Gu ◽  
Ming Fu Zhang
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Mapping Method ◽  
Face Database ◽  
New Approach ◽  
Maximum Variance

Li et al. [Pattern Recognition 41 (2008) 3287 -- 329 proposed the constrained maximum variance mapping method. The CMVM is globally maximizing the distances between different manifolds. We find out that globally minimizing the distances between the same manifolds can have better recognition than CMVM method on the Yale face database, ORL face database and UMIST face database. Hence we propose to use an inverse constrained maximum variance mapping method (ICMVM) which can be seen as the inverse Laplacian Fisher discriminate criteria. Experiment results suggest that this new approach performs well.

Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

scholarly journals Face Recognition Using the SR-CNN Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4237 ◽  
Author(s):  
Yu-Xin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fang-Qing Wen ◽  
Guan-Qun Sheng ◽  
...  
Keyword(s):  
Face Recognition ◽  
Target Position ◽  
Operation Time ◽  
True Positive Rate ◽  
Processing Unit ◽  
True Positive ◽  
Face Database ◽  
Acceleration Ratio ◽  
Computational Performance ◽  
Positive Rate

In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature transform (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model for an optimal computational performance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected for experiments. It turns out that the true positive rate is improved by 10.97–13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5–6 times for the LFW face database. For the self-collection, the true positive rate increased by 12.65–15.31%, and the acceleration ratio improved by a factor of 6–7.

scholarly journals A Novel Approach of Low-Light Image Denoising for Face Recognition

2014 ◽  
Vol 6 ◽  
pp. 256790
Author(s):  
Yimei Kang ◽  
Wang Pan
Keyword(s):  
Face Recognition ◽  
Image Denoising ◽  
Recognition Rate ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Face Database ◽  
Low Light ◽  
Very Low Frequency ◽  
Light Image

Illumination variation makes automatic face recognition a challenging task, especially in low light environments. A very simple and efficient novel low-light image denoising of low frequency noise (DeLFN) is proposed. The noise frequency distribution of low-light images is presented based on massive experimental results. The low and very low frequency noise are dominant in low light conditions. DeLFN is a three-level image denoising method. The first level denoises mixed noises by histogram equalization (HE) to improve overall contrast. The second level denoises low frequency noise by logarithmic transformation (LOG) to enhance the image detail. The third level denoises residual very low frequency noise by high-pass filtering to recover more features of the true images. The PCA (Principal Component Analysis) recognition method is applied to test recognition rate of the preprocessed face images with DeLFN. DeLFN are compared with several representative illumination preprocessing methods on the Yale Face Database B, the Extended Yale face database B, and the CMU PIE face database, respectively. DeLFN not only outperformed other algorithms in improving visual quality and face recognition rate, but also is simpler and computationally efficient for real time applications.

scholarly journals Pattern recognition using feature feedback: Application to face recognition

2010 ◽  
Vol 8 (1) ◽  
pp. 141-148 ◽  
Author(s):  
Gu-Min Jeong ◽  
Hyun-Sik Ahn ◽  
Sang-Il Choi ◽  
Nojun Kwak ◽  
Chanwoo Moon
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Feature Feedback

New Approach for Segmentation and Pattern Recognition of Jacquard Images

2005 ◽  
pp. 965-970
Author(s):  
Zhilin Feng ◽  
Jianwei Yin ◽  
Zhaoyang He ◽  
Wuheng Zuo ◽  
Jinxiang Dong
Keyword(s):  
Pattern Recognition ◽  
New Approach

Fast Face Recognition on GPUs

2014 ◽  
Vol 905 ◽  
pp. 543-547
Author(s):  
Yi Lei ◽  
Xiao Ya Fan ◽  
Meng Zhang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Speed ◽  
Principal Component ◽  
Great Accuracy ◽  
Analysis Algorithm ◽  
Training Images ◽  
Principal Component Analysis Algorithm

Face recognition is popular in the field of pattern recognition and image processing. However, traditional recognition technologies spend too long there are a lot of images to be recognized or trained for great accuracy in the recognition. Parallel computing is an effective way to improve the processing speed. With the improvement of GPU performance, its widely applied in computing-concentrated data operations. This paper presents a study of performance speedup achieved by applying GPU for face recognition based on PCA (Principal Component Analysis) algorithm. We successfully accelerated the testing phase by 6868-folds compared to a sequential C implementation when it has 100 test images and 2400 training images.

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