Name
Abstract | ||
|---|---|---|
Kinship inferred from pairs of facial images provides contextual information for various applications including forensics, genealogical science research, image retrieval, and image database annotation. Because automatically identifying and predicting siblings from pairs of facial images with high confidence remains a challenge in computer vision applications, we propose in this paper a robust framework for detecting siblings from a pair of images, based upon how closely one image's feature set matches that of another. In calculating similarity for a given pair of images, our algorithm predicts a sibling pair only when matched-feature vectors are above a defined similarity metric threshold (85%). We illustrate a combination of metaheuristic and support vector machine methods for recognition wherein distance-based features can be used to build a hidden Markov model. A further contribution of the work is the development of a novel classification strategy that fuses a genetic algorithm and a support vector machine in order to identify siblings. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CISS.2015.7086888 | CISS |
Keywords | Field | DocType |
hidden markov model,computer vision,genetic algorithm,image classification,feature extraction,vectors,genetic algorithms,discrete cosine transform,face,support vector machine,support vector machines,measurement,face recognition,hidden markov models | Computer vision,Facial recognition system,Feature vector,Pattern recognition,Computer science,Support vector machine,Discrete cosine transform,Image retrieval,Artificial intelligence,Relevance vector machine,Hidden Markov model,Genetic algorithm | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sherin M. Mathews | 1 | 6 | 1.51 |
| Chandra Kambhamettu | 2 | 858 | 80.83 |
| Kenneth E. Barner | 3 | 812 | 70.19 |