1. Recognizing Families through Images with Pretrained Encoder
- Author
-
Tuan-Duy H. Nguyen, Huu-Nghia H. Nguyen, and Hieu Dao
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,0209 industrial biotechnology ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Feature extraction ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,computer.software_genre ,Facial recognition system ,Machine Learning (cs.LG) ,Task (project management) ,020901 industrial engineering & automation ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Kinship ,business.industry ,Image and Video Processing (eess.IV) ,Electrical Engineering and Systems Science - Image and Video Processing ,Visualization ,Feature (computer vision) ,Task analysis ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,Encoder ,Natural language processing - Abstract
Kinship verification and kinship retrieval are emerging tasks in computer vision. Kinship verification aims at determining whether two facial images are from related people or not, while kinship retrieval is the task of retrieving possible related facial images to a person from a gallery of images. They introduce unique challenges because of the hidden relations and features that carry inherent characteristics between the facial images. We employ 3 methods, FaceNet, Siamese VGG-Face, and a combination of FaceNet and VGG-Face models as feature extractors, to achieve the 9th standing for kinship verification and the 5th standing for kinship retrieval in the Recognizing Family in The Wild 2020 competition. We then further experimented using StyleGAN2 as another encoder, with no improvement in the result., Will appear as part of RFIW2020 in the Proceedings of 2020 International Conference on Automatic Face and Gesture Recognition (IEEE AMFG)
- Published
- 2020
- Full Text
- View/download PDF