Name
Abstract | ||
|---|---|---|
Recently there has been a large explosive growth of image data on social networks and how to use computer vision and machine learning technology to verify people relationships on these huge amount of human-centered image data remains a challenging issue. Remarkably, there have been few research attempts to analyze the possible human relationships on images, especially kin relationships. In this paper, we tackle a challenging and relatively new issue in kinship classification: determining the family that a query face image belongs to. To address this challenge, we propose a kinship classification method in three steps: (l)Discriminative patches are detected automatically in the facial landmark regions. (2) Appearance features, Histogram of Gradient (HOG), Scale-Invariant Feature Transform (SIFT) and Four-Patch Local Binary Pattern (FPLBP) are extracted from these patches respectively, and then we concatenate the features to create a high-dimensional feature vector. (3) Linear Support Vector Machine (SVM) with polynomial kernel is adopted to accomplish kinship classification task. Experimental evaluation results on Cornell Family 101 dataset demonstrate that our proposed method significantly outperforms the state-of-the-art kinship classification approaches. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/VCIP.2014.7051528 | VCIP |
Keywords | Field | DocType |
fplbp,human-centered image data,query face image,four-patch local binary pattern,machine learning technology,linear support vector machine,face recognition,social networks,learning (artificial intelligence),facial landmark regions,appearance features,people relationships,svm,kinship classification,discriminative facial patches,polynomial kernel,high-dimensional feature vector,feature extraction,image classification,image retrieval,hog,computer vision,kin relationships,scale-invariant feature transform,transforms,support vector machine,social networking (online),histogram of gradient,polynomials,support vector machines,cornell family 101 dataset,sift | Structured support vector machine,Computer science,Local binary patterns,Artificial intelligence,Computer vision,Feature vector,Bag-of-words model in computer vision,Pattern recognition,Feature (computer vision),Support vector machine,Feature extraction,Linear classifier,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Dong | 1 | 0 | 0.68 |
| Xiang Ao | 2 | 0 | 0.34 |
| Song-zhi Su | 3 | 61 | 8.53 |
| Shaozi Li | 4 | 403 | 54.27 |