Face-graph matching for classifying groups of people

Shu, Henry; Gallagher, Andrew; Chen, Huizhong; Chen, Tsuhan

doi:10.1109/icip.2013.6738500

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Social life endows various social appearances to people. Some research focus on urban tribes in daily life [18], social categories defined by Wikipedia [19,20], and popular groups such as "Loli", "Syota" and "Goddess" which are mostly derived from social networks [21]. These fine-grained categorization uses body/face positions and attributes such as age, face appearance, hair style, clothing style and so on.…”

Section: Related Workmentioning

confidence: 99%

A Domain Based Approach to Social Relation Recognition

Sun

Schiele

Fritz

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domainbased theory from social psychology is a great starting point to systematically approach this problem. The theory provides coverage of all aspects of social relations and equally is concrete and predictive about the visual attributes and behaviors defining the relations included in each domain. We provide the first dataset built on this holistic conceptualization of social life that is composed of a hierarchical label space of social domains and social relations. We also contribute the first models to recognize such domains and relations and find superior performance for attribute based features. Beyond the encouraging performance of the attribute based approach, we also find interpretable features that are in accordance with the predictions from social psychology literature. Beyond our findings, we believe that our contributions more tightly interleave visual recognition and social psychology theory that has the potential to complement the theoretical work in the area with empirical and data-driven models of social life.

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Section: Related Workmentioning

confidence: 99%

A Domain Based Approach to Social Relation Recognition

Sun

Schiele

Fritz

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Knowing genders of subjects is also crucial for people to identify social relationships of subjects in group photos. Gender estimation has been incorporated for pairwise social relationship classification [10] [14].…”

Section: Related Workmentioning

confidence: 99%

Spatial Face Context with Gender Information for Group Photo Similarity Assessment

Chiu

Hsu

Huang

2014

2014 22nd International Conference on Pattern Recognition

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Spatial arrangements of people in a photo reflect how people regard themselves in a group. Comparing spatial arrangements of subjects in two group photos can help computer understand similar social semantics and events. In this paper, we incorporate gender information with spatial arrangements of subjects to assess the visual similarity between two group photos. For each group photo, detected faces are represented by a spatial face context. Then, corresponding vertices of spatial face contexts of two group photos are retrieved by graph matching. Gender information and relative face positions are applied to the graph matching results to compute the similarity score. The experimental results show that our method can identify group photos with similar genders of subjects and spatial arrangements compared to the state-of-the-art methods.

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“…Several works analyzed the contexts to study the structure of scenes in group photos [3,6,7]. Some researchers utilized them to classify group types [2,4,8,9], retrieve similar group photos [10][11][12], discover social relations [5,13], or predict occupations [14].…”

Section: Social Context In Photographsmentioning

confidence: 99%

Discriminative subgraphs for discovering family photos

2016

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We propose to use discriminative subgraphs to discover family photos from group photos in an efficient and effective way. Group photos are represented as face graphs by identifying social contexts such as age, gender, and face position. The previous work utilized bag-of-word models and considered frequent subgraphs from all group photos as features for classification. This approach, however, produces numerous subgraphs, resulting in high dimensions. Furthermore, some of them are not discriminative. To solve these issues, we adopt a state-of-the-art, frequent subgraph mining method that removes nondiscriminative subgraphs.We also use TF-IDF normalization, which is more suitable for the bag-ofword model. To validate our method, we experiment in two datasets.Our method shows consistently better performance, higher accuracy in lower feature dimensions, compared to the previous method. We also integrate our method with the recent Microsoft face recognition API and release it in a public website.

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Face-graph matching for classifying groups of people

Cited by 6 publications

References 15 publications

A Domain Based Approach to Social Relation Recognition

A Domain Based Approach to Social Relation Recognition

Spatial Face Context with Gender Information for Group Photo Similarity Assessment

Discriminative subgraphs for discovering family photos

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