Laplacian-Uniform Mixture-Driven Iterative Robust Coding With Applications to Face Recognition Against Dense Errors

2020 ◽  
Vol 31 (9) ◽  
pp. 3620-3633
Author(s):  
Huicheng Zheng ◽  
Dajun Lin ◽  
Lina Lian ◽  
Jiayu Dong ◽  
Peipei Zhang
Keyword(s):  
Face Recognition ◽  
Robust Coding

Bayesian Robust Coding Face Recognition Algorithm Based on New Dictionary

2014 ◽  
Vol 644-650 ◽  
pp. 4080-4083
Author(s):  
Ye Cai Guo ◽  
Ling Hua Zhang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Binary Image ◽  
Test Image ◽  
Face Database ◽  
Robust Coding ◽  
The Face ◽  
Isolated Points ◽  
Rate Comparison

In order to overcome the defects that the face recognition rate can be greatly reduced in the existing uncontrolled environments, Bayesian robust coding for face recognition based on new dictionary was proposed. In this proposed algorithm, firstly a binary image is gained by gray threshold transformation and a more clear image without some isolated points can be obtained via smoothing, secondly a new dictionary can be reconstructed via fusing the binary image with the original training dictionary, finally the test image can be classified as the existing class via Bayesian robust coding. The experimental results based on AR face database show that the proposed algorithm has higher face recognition rate comparison with RRC and RSC algorithm.

scholarly journals Structured regularized robust coding for face recognition

2016 ◽  
Vol 216 ◽  
pp. 18-27 ◽  
Author(s):  
Xing Wang ◽  
Meng Yang ◽  
Linlin Shen
Keyword(s):  
Face Recognition ◽  
Robust Coding

scholarly journals Regularized Robust Coding for Face Recognition

2013 ◽  
Vol 22 (5) ◽  
pp. 1753-1766 ◽  
Author(s):  
Meng Yang ◽  
Lei Zhang ◽  
Jian Yang ◽  
David Zhang
Keyword(s):  
Face Recognition ◽  
Robust Coding

Feature Representation Method Based on Kirsch Masks Filter for Face Recognition

2014 ◽  
Vol 989-994 ◽  
pp. 4209-4212
Author(s):  
Zhao Kui Li ◽  
Yan Wang
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Feature Representation ◽  
Linear Discriminant ◽  
Robust Coding ◽  
Gabor Features ◽  
Robust Face Recognition ◽  
Representation Method ◽  
Low Dimensional ◽  
Direction Image

In this paper, a feature representation method based on Kirsch masks filter for face recognition is proposed. We firstly obtain eight direction images by performing Kirsch masks filter. For each direction image, the low-dimensional feature vector is computed by Linear Discriminant Analysisis. Then, a fusion strategy is used to combine different direction image according to their respective salience. Experimental results show that our methods significantly outperform popular methods such as Gabor features, Local Binary Patterns, Regularized Robust Coding (RRC), and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

Face Inversion Effect Emerges Under Critical Configural Discrepancy

2010 ◽  
Vol 69 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Jisien Yang ◽  
Adrian Schwaninger
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Underlying Mechanism ◽  
Inversion Effect ◽  
Configural Processing ◽  
Major Contributor ◽  
Face Inversion Effect ◽  
Configural Information ◽  
Face Inversion ◽  
The Face

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.

Perceptual Effects and Recollective Experience in Face Recognition

2005 ◽  
Vol 52 (3) ◽  
pp. 224-231 ◽  
Author(s):  
Chrisanthi Nega
Keyword(s):  
Face Recognition ◽  
Recognition Memory ◽  
Congruency Effect ◽  
Facial Recognition ◽  
Memory Systems ◽  
Study Time ◽  
Perceptual Fluency ◽  
Recollective Experience ◽  
Trace Strength ◽  
Size Congruency

Abstract. Four experiments were conducted investigating the effect of size congruency on facial recognition memory, measured by remember, know and guess responses. Different study times were employed, that is extremely short (300 and 700 ms), short (1,000 ms), and long times (5,000 ms). With the short study time (1,000 ms) size congruency occurred in knowing. With the long study time the effect of size congruency occurred in remembering. These results support the distinctiveness/fluency account of remembering and knowing as well as the memory systems account, since the size congruency effect that occurred in knowing under conditions that facilitated perceptual fluency also occurred independently in remembering under conditions that facilitated elaborative encoding. They do not support the idea that remember and know responses reflect differences in trace strength.

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