| Title | Person Identification from Visual Aesthetics Using Gene Expression Programming |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Sieu, Brandon, Gavrilova, Marina |
| Conference Name | 2019 International Conference on Cyberworlds (CW) |
| Keywords | aesthetic feature dimensionality reduction, aesthetics, behavioral biometric, Behavioral biometrics, biometrics (access control), cybersecurity, discriminatory features, feature extraction, feature recombination, Flickr users, Gene expression, gene expression programming, Genetics, Head, Human Behavior, Human Behavior and Cybersecurity, human computer interaction, online human interactions, pattern classification, Pattern recognition, person identification, personal activities, physical traits, principal component analysis, professional activities, Programming, pubcrawl, social networking (online), tree-based genetic approach, trees (mathematics), users aesthetic preferences, visual aesthetic, visualization |
| Abstract | The last decade has witnessed an increase in online human interactions, covering all aspects of personal and professional activities. Identification of people based on their behavior rather than physical traits is a growing industry, spanning diverse spheres such as online education, e-commerce and cyber security. One prominent behavior is the expression of opinions, commonly as a reaction to images posted online. Visual aesthetic is a soft, behavioral biometric that refers to a person's sense of fondness to a certain image. Identifying individuals using their visual aesthetics as discriminatory features is an emerging domain of research. This paper introduces a new method for aesthetic feature dimensionality reduction using gene expression programming. The advantage of this method is that the resulting system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40000 images demonstrates a 94% accuracy of identity recognition based solely on users' aesthetic preferences. This outperforms the best-known method by 13.5%. |
| DOI | 10.1109/CW.2019.00053 |
| Citation Key | sieu_person_2019 |
Person Identification from Visual Aesthetics Using Gene Expression Programming