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 domain-based 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.
Relationships among family members are the most basic social relations for human. There exist a large number of studies about family member recognition and kinship verification @cite_36 @cite_20 @cite_44 @cite_13 @cite_12 @cite_5 @cite_11 @cite_31 @cite_17 . Most of these works focus on familial relations: husband-wife, parents-children, siblings, grandparents-grandchildren. Researchers leverage certain visual patterns exhibited in these relations. For instance, for two people in a wife-husband relation, husband's face is usually in a higher position than wife's @cite_36 @cite_44 . Not only the location information but also the facial appearance, attributes and landmarks are essential features to verify family members. Dehghan al @cite_31 learn the optimal face features to answer the question of Do offspring resemble their parents?''. Singla al @cite_20 propose some attribute-related assumptions, , two people of similar age and opposite gender appearing together are spouses.
@cite_36 @cite_17 @cite_44 @cite_5 @cite_31 @cite_20 @cite_13 @cite_12 @cite_11
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The people in an image are generally not strangers, but instead often share social relationships such as husband-wife, siblings, grandparent-child, father-child, or mother-child. Further, the social relationship between a pair of people influences the relative position and appearance of the people in the image. This paper explores using familial social relationships as context for recognizing people and for recognizing the social relationships between pairs of people. We introduce a model for representing the interaction between social relationship, facial appearance, and identity. We show that the family relationship a pair of people share influences the relative pairwise features between them. The experiments on a set of personal collections show significant improvement in people recognition is achieved by modeling social relationships, even in a weak label setting that is attractive in practical applications. Furthermore, we show the social relationships are effectively recognized in images from a separate test image collection. We tackle the challenge of kinship verification using novel feature extraction and selection methods, automatically classifying pairs of face images as “related” or “unrelated” (in terms of kinship). First, we conducted a controlled online search to collect frontal face images of 150 pairs of public figures and celebrities, along with images of their parents or children. Next, we propose and evaluate a set of low-level image features for this classification problem. After selecting the most discriminative inherited facial features, we demonstrate a classification accuracy of 70.67 on a test set of image pairs using K-Nearest-Neighbors. Finally, we present an evaluation of human performance on this problem. Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10 depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models. We propose a new, challenging, problem in kinship classification: recognizing the family that a query person belongs to from a set of families. We propose a novel framework for recognizing kinship by modeling this problem as that of reconstructing the query face from a mixture of parts from a set of families. To accomplish this, we reconstruct the query face from a sparse set of samples among the candidate families. Our sparse group reconstruction roughly models the biological process of inheritance: a child inherits genetic material from two parents, and therefore may not appear completely similar to either parent, but is instead a composite of the parents. The family classification is determined based on the reconstruction error for each family. On our newly collected “Family101” dataset, we discover links between familial traits among family members and achieve state-of-the-art family classification performance.