scholarly journals The influence of processing instructions at encoding and retrieval on face recognition accuracy

1998 ◽  
Vol 4 (2) ◽  
pp. 89-106 ◽  
Author(s):  
Garrett L. Berman ◽  
Brian L. Cutler
Keyword(s):  
Face Recognition ◽  
Recognition Accuracy ◽  
Encoding And Retrieval

scholarly journals Two Factors in Face Recognition: Whether You Know the Person’s Face and Whether You Share the Person’s Race

Perception ◽  
2021 ◽  
pp. 030100662110140
Author(s):  
Xingchen Zhou ◽  
A. M. Burton ◽  
Rob Jenkins
Keyword(s):  
Face Recognition ◽  
Factorial Design ◽  
Performance Measures ◽  
Recognition Accuracy ◽  
The Other ◽  
Familiarity Effect ◽  
Race Effect ◽  
Two Factors ◽  
Unfamiliar Face

One of the best-known phenomena in face recognition is the other-race effect, the observation that own-race faces are better remembered than other-race faces. However, previous studies have not put the magnitude of other-race effect in the context of other influences on face recognition. Here, we compared the effects of (a) a race manipulation (own-race/other-race face) and (b) a familiarity manipulation (familiar/unfamiliar face) in a 2 × 2 factorial design. We found that the familiarity effect was several times larger than the race effect in all performance measures. However, participants expected race to have a larger effect on others than it actually did. Face recognition accuracy depends much more on whether you know the person’s face than whether you share the same race.

Navon Letters Affect Face Learning and Face Retrieval

2009 ◽  
Vol 56 (4) ◽  
pp. 258-264 ◽  
Author(s):  
Michael B. Lewis ◽  
Claire Mills ◽  
Peter J. Hills ◽  
Nicola Weston
Keyword(s):  
Face Recognition ◽  
Present Experiment ◽  
Recognition Accuracy ◽  
Underlying Mechanism ◽  
Performance Accuracy ◽  
Face Retrieval ◽  
Large Letter

Identifying the local letters of a Navon letter (a large letter made up of smaller different letters) prior to recognition causes impairment in accuracy, while identifying the global letters of a Navon letter causes an enhancement in recognition accuracy ( Macrae & Lewis, 2002 ). This effect may result from a transfer-inappropriate processing shift (TIPS) ( Schooler, 2002 ). The present experiment extends research on the underlying mechanism of this effect by exploring this Navon effect on face learning as well as face recognition. The results of the two experiments revealed that when the Navon task used at retrieval was the same as that used at encoding then the performance accuracy is enhanced, whereas when the processing operations mismatch at retrieval and at encoding, this impairs recognition accuracy. These results provide support for the TIPS explanation of the Navon effect.

Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

2016 ◽  
Vol 30 (08) ◽  
pp. 1656005 ◽  
Author(s):  
Jiadi Li ◽  
Zhenxue Chen ◽  
Chengyun Liu
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Principal Component ◽  
Low Resolution ◽  
Multi Scale ◽  
Face Features ◽  
Weighted Principal Component ◽  
Pca Algorithm

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12[Formula: see text]10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

scholarly journals Analysis of Gender Inequality In Face Recognition Accuracy

2020 ◽  
Author(s):  
Vitor Albiero ◽  
K.S. Krishnapriya ◽  
Kushal Vangara ◽  
Kai Zhang ◽  
Michael C. King ◽  
...  
Keyword(s):  
Face Recognition ◽  
Gender Inequality ◽  
Recognition Accuracy

scholarly journals Normalization of Facial Occlusion in Face Recognition

2019 ◽  
Vol 8 (11) ◽  
pp. 2523-2527
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Face Detection ◽  
Error Rate ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Principal Component ◽  
Normalization Method ◽  
Analysis Algorithm ◽  
Principal Component Analysis Algorithm

Face recognition accuracy is determined by face detection results. Detected faces will be in view of clear and occlusion faces. If detected face has occlusion than recognition accuracy is reduced. This research is directed to increase recognition rate when detected occlusion face. In this paper is proposed normalization occlusion faces by Principal component analysis algorithm. After applying normalization method in occlusion faces false reject error rate is decreased.

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