Sparse Learning for Face Recognition with Social Context

2013 ◽  
pp. 820-826
Author(s):  
Jie Gui ◽  
Jian-Xun Mi ◽  
Ying-Ke Lei ◽  
Hong-Qiang Wang
Keyword(s):  
Face Recognition ◽  
Social Context ◽  
Sparse Learning

Class, Race, and the Face: Social Context Modulates the Cross-Race Effect in Face Recognition

2007 ◽  
Vol 34 (2) ◽  
pp. 260-274 ◽  
Author(s):  
Edwin R. Shriver ◽  
Steven G. Young ◽  
Kurt Hugenberg ◽  
Michael J. Bernstein ◽  
Jason R. Lanter
Keyword(s):  
Face Recognition ◽  
Social Context ◽  
Race Effect ◽  
The Face ◽  
The Cross

New Sparse Facial Feature Description Model Based on Salience Evaluation of Regions and Features

2015 ◽  
Vol 29 (05) ◽  
pp. 1556007 ◽  
Author(s):  
Yue Zhao ◽  
Jianbo Su
Keyword(s):  
Face Recognition ◽  
Evaluation Method ◽  
Facial Feature ◽  
Experimental Results ◽  
Sparse Learning ◽  
Feature Description ◽  
Model Based ◽  
Face Images ◽  
Proposed Model ◽  
Structured Sparse Learning

Some regions (or blocks) and their affiliated features of face images are normally of more importance for face recognition. However, the variety of feature contributions, which exerts different saliency on recognition, is usually ignored. This paper proposes a new sparse facial feature description model based on salience evaluation of regions and features, which not only considers the contributions of different face regions, but also distinguishes that of different features in the same region. Specifically, the structured sparse learning scheme is employed as the salience evaluation method to encourage sparsity at both the group and individual levels for balancing regions and features. Therefore, the new facial feature description model is obtained by combining the salience evaluation method with region-based features. Experimental results show that the proposed model achieves better performance with much lower feature dimensionality.

scholarly journals Self-face recognition in social context

2011 ◽  
Vol 33 (6) ◽  
pp. 1364-1374 ◽  
Author(s):  
Motoaki Sugiura ◽  
Yuko Sassa ◽  
Hyeonjeong Jeong ◽  
Keisuke Wakusawa ◽  
Kaoru Horie ◽  
...  
Keyword(s):  
Face Recognition ◽  
Social Context

scholarly journals More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Qiaoling Han ◽  
Jianbo Su ◽  
Yue Zhao
Keyword(s):  
Face Recognition ◽  
Learning Strategy ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Facial Feature ◽  
Sparse Learning ◽  
Learning Method ◽  
Training Set ◽  
Feature Description ◽  
The Face

In the actual face recognition applications, the sample sets are updated constantly. However, most of the face recognition models with learning strategy do not consider this fact and using a fixed training set to learn the face recognition models for once. Besides that, the testing samples are discarded after the testing process is completed. Namely, the training and testing processes are separated and the later does not give a feedback to the former for better recognition results. To attenuate these problems, this paper proposed an online sparse learning method for face recognition. It can update the salience evaluation vector in real time to construct a dynamical facial feature description model. Also, a strategy for updating the gallery set is proposed in this proposed method. Both the dynamical facial feature description model and the gallery set are employed to recognize faces. Experimental results show that the proposed method improves the face recognition accuracy, comparing with the classical learning models and other state-of-the-art face recognition methods.

Harnessing social context for improved face recognition

2015 ◽  
Author(s):  
Romil Bhardwaj ◽  
Gaurav Goswami ◽  
Richa Singh ◽  
Mayank Vatsa
Keyword(s):  
Face Recognition ◽  
Social Context

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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