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Multi-cue Augmented Face Clustering
- Source :
- ACM Multimedia
- Publication Year :
- 2015
- Publisher :
- ACM, 2015.
-
Abstract
- Face clustering is an important but challenging task since facial images always have huge variation due to change in facial expressions, head poses and partial occlusions, etc. Moreover, face clustering is actually an unsupervised problem which makes it more difficult to reach an accurate result. Fortunately, there are some cues that can be used to improve clustering performance. In this paper, two types of cues are employed. The first one is pairwise constraints: must-link and cannot-link constraints, which can be extracted from the temporal and spatial knowledge of data. The other is that each face is associated with a series of attributes (i.e, gender) which can contribute discrimination among faces. To take advantage of the above cues, we propose a new algorithm, Multi-cue Augmented Face Clustering (McAFC), which effectively incorporates the cues via graph-guided sparse subspace clustering technique. Specially, facial images from the same individual are encouraged to be connected while faces from different persons are restrained to be connected. Experiments on three face datasets from real-world videos show the improvements of our algorithm over the state-of-the-art methods.
- Subjects :
- Facial expression
Fuzzy clustering
Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
Sparse approximation
Task (project management)
ComputingMethodologies_PATTERNRECOGNITION
Face (geometry)
Computer vision
Artificial intelligence
business
Cluster analysis
Face detection
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 23rd ACM international conference on Multimedia
- Accession number :
- edsair.doi...........ebc39894487ed40169ecb519b4df591c