A Rough Sets Approach for Personalized Support of Face Recognition

Hepting, Daryl H.; Maciag, Timothy; Spring, Richard; Arbuthnott, Katherine D.; Ślęzak, Dominik

doi:10.1007/978-3-642-10646-0_24

Cited by 5 publications

(2 citation statements)

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“…The stimuli used in the card sorting activity combined two sets of facial photos: one set of 178 Caucasian male subjects and the other set of 178 First Nations male subjects. All photos are identified by a 4 digit code, which is a departure from earlier publications describing the card sorting study (see Hepting et al [4,5]). The first digit indicates the stimulus set (1: Caucasian, 2: First Nations) followed by 3 digits to indicate the sequence number in that stimulus set (0-177).…”

Section: A Simple Linear Model Of Three-way Analysismentioning

confidence: 99%

A Linear Model for Three-Way Analysis of Facial Similarity

Hepting

Amer

Yao

2018

Communications in Computer and Information Science

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Card sorting was used to gather information about facial similarity judgments. A group of raters put a set of facial photos into an unrestricted number of different piles according to each rater's judgment of similarity. This paper proposes a linear model for 3-way analysis of similarity. An overall rating function is a weighted linear combination of ratings from individual raters. A pair of photos is considered to be similar, dissimilar, or divided, respectively, if the overall rating function is greater than or equal to a certain threshold, is less than or equal to another threshold, or is between the two thresholds. The proposed framework for 3-way analysis of similarity is complementary to studies of similarity based on features of photos.

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Section: A Simple Linear Model Of Three-way Analysismentioning

confidence: 99%

A Linear Model for Three-Way Analysis of Facial Similarity

Hepting

Amer

Yao

2018

Communications in Computer and Information Science

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“…In such cases, one may be mostly interested in weak monotonicity but not in strict monotonicity. Actually, the cases when the measure does not change while changing the partition into a more or less detailed one are of special importance for feature selection, feature subset selection, feature extraction, and feature reduction in knowledge discovery (see, for example, [7,11,31]). In view of this, let us introduce a weak version of Definition 2 as follows.…”

Section: Definitionmentioning

confidence: 99%

An Axiomatic Approach to the Roughness Measure of Rough Sets

Zhu

2011

Fundamenta Informaticae

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In Pawlak's rough set theory, a set is approximated by a pair of lower and upper approximations. To measure numerically the roughness of an approximation, Pawlak introduced a quantitative measure of roughness by using the ratio of the cardinalities of the lower and upper approximations. Although the roughness measure is effective, it has the drawback of not being strictly monotonic with respect to the standard ordering on partitions. Recently, some improvements have been made by taking into account the granularity of partitions. In this paper, we approach the roughness measure in an axiomatic way. After axiomatically defining roughness measure and partition measure, we provide a unified construction of roughness measure, called strong Pawlak roughness measure, and then explore the properties of this measure. We show that the improved roughness measures in the literature are special instances of our strong Pawlak roughness measure and introduce three more strong Pawlak roughness measures as well. The advantage of our axiomatic approach is that some properties of a roughness measure follow immediately as soon as the measure satisfies the relevant axiomatic definition.

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