3D Facial Similarity Measure Based on Deformation Field

Automated 3D facial similarity measure is a challenging and valuable research topic in anthropology and computer graphics. It is widely used in various fields, such as criminal investigation, kinship confirmation, and face recognition. This paper proposes a 3D facial similarity measure method based on a combination of geodesic and curvature features. Firstly, a geodesic network is generated for each face with geodesics and iso-geodesics determined and these network points are adopted as the correspondence across face models. Then, four metrics associated with curvatures, that is, the mean curvature, Gaussian curvature, shape index, and curvedness, are computed for each network point by using a weighted average of its neighborhood points. Finally, correlation coefficients according to these metrics are computed, respectively, as the similarity measures between two 3D face models. Experiments of different persons’ 3D facial models and different 3D facial models of the same person are implemented and compared with a subjective face similarity study. The results show that the geodesic network plays an important role in 3D facial similarity measure. The similarity measure defined by shape index is consistent with human’s subjective evaluation basically, and it can measure the 3D face similarity more objectively than the other indices.

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Haunted by a Doppelgänger

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000221 ◽

2014 ◽

Vol 61 (1) ◽

pp. 12-22 ◽

Cited By ~ 11

Author(s):

Bettina von Helversen ◽

Stefan M. Herzog ◽

Jörg Rieskamp

Keyword(s):

Relevant Information ◽

Psychological Research ◽

Decision Makers ◽

Human Judgment ◽

Rule Based ◽

New Employees ◽

High Performing ◽

Facial Similarity ◽

Similarity Effect ◽

Judgment Strategy

Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

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Handling WSD using Hierarchical Clustering Algorithm with sentences

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset1841120 ◽

2018 ◽

pp. 83-88

Author(s):

Mohana Priya K ◽

Pooja Ragavi S ◽

Krishna Priya G

Keyword(s):

Hierarchical Clustering ◽

Similarity Measure ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Cosine Similarity Measure ◽

Hierarchical Clustering Algorithm ◽

Multiple Levels ◽

Pos Tagger ◽

Sentence Clustering ◽

The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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