Novel Texture Descriptor Family for Face Recognition

LEDTD: Local edge direction and texture descriptor for face recognition

Signal Processing Image Communication ◽

10.1016/j.image.2015.12.003 ◽

2016 ◽

Vol 41 ◽

pp. 40-45 ◽

Cited By ~ 11

Author(s):

Jing Li ◽

Nong Sang ◽

Changxin Gao

Keyword(s):

Face Recognition ◽

Texture Descriptor ◽

Edge Direction ◽

Local Edge

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Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences

Sensors ◽

10.3390/s19051048 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1048 ◽

Cited By ~ 2

Author(s):

Óscar García-Olalla ◽

Laura Fernández-Robles ◽

Enrique Alegre ◽

Manuel Castejón-Limas ◽

Eduardo Fidalgo

Keyword(s):

Face Recognition ◽

State Of The Art ◽

Local Binary Patterns ◽

Statistical Information ◽

The State ◽

Texture Descriptor ◽

Texture Recognition ◽

Material Recognition ◽

Multi Scale ◽

Hit Rate

This paper presents a new texture descriptor booster, Complete Local Oriented Statistical Information Booster (CLOSIB), based on statistical information of the image. Our proposal uses the statistical information of the texture provided by the image gray-levels differences to increase the discriminative capability of Local Binary Patterns (LBP)-based and other texture descriptors. We demonstrated that Half-CLOSIB and M-CLOSIB versions are more efficient and precise than the general one. H-CLOSIB may eliminate redundant statistical information and the multi-scale version, M-CLOSIB, is more robust. We evaluated our method using four datasets: KTH TIPS (2-a) for material recognition, UIUC and USPTex for general texture recognition and JAFFE for face recognition. The results show that when we combine CLOSIB with well-known LBP-based descriptors, the hit rate increases in all the cases, introducing in this way the idea that CLOSIB can be used to enhance the description of texture in a significant number of situations. Additionally, a comparison with recent algorithms demonstrates that a combination of LBP methods with CLOSIB variants obtains comparable results to those of the state-of-the-art.

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HJDLBP: A novel texture descriptor and its application in face recognition

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) ◽

10.1109/aisp.2015.7123511 ◽

2015 ◽

Author(s):

Sayed Mohamad Tabatabaei ◽

Abdollah Chalechale

Keyword(s):

Face Recognition ◽

Texture Descriptor

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Face Inversion Effect Emerges Under Critical Configural Discrepancy

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000018 ◽

2010 ◽

Vol 69 (3) ◽

pp. 161-167 ◽

Cited By ~ 3

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.

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Perceptual Effects and Recollective Experience in Face Recognition

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169.52.3.224 ◽

2005 ◽

Vol 52 (3) ◽

pp. 224-231 ◽

Cited By ~ 7

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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