Statistical Models of Shape and Texture for Face Recognition

Artificial intelligence (AI) is a huge headway in innovation that has everybody talking about its energizing guarantees in the innovation world. With regards to AI, it additionally incorporates its territories, for example, AI (ML). While AI could be portrayed as the capacity of machines to settle on shrewd human-like choices and improve over the long run, ML includes building models, generally statistical models that give prescient outcomes and can be developed. Many are not extremely educated about this area. While this is true, there is something else entirely to it from face recognition, fingerprints recognition, chat-bots, predictive business models, and sentimental analysis. Beforehand, AI joining in the product advancement was simply conceivable to the huge organizations that had the assets to recruit exceptionally qualified experts. Over the long run, AI structures with high deliberation levels have been created, and with few coding lines in any programming language of the decision, one can have the option to enter in various fields.

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Statistical Models: Theory and Practice

10.1017/cbo9781139165495 ◽

2005 ◽

Cited By ~ 204

Author(s):

David Freedman

Keyword(s):

Statistical Models ◽

Theory And Practice

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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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