Facing scalability: Naming faces in an online social network
Pattern Recognition
Facial emotion recognition with expression energy
Proceedings of the 14th ACM international conference on Multimodal interaction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Motion interchange patterns for action recognition in unconstrained videos
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Force work induced metric for face verification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Exploring the similarities of neighboring spatiotemporal points for action pair matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
The case for onloading continuous high-datarate perception to the phone
HotOS'13 Proceedings of the 14th USENIX conference on Hot Topics in Operating Systems
Texture recognition by using a non-linear kernel
International Journal of Computer Applications in Technology
I Can Help You Change! An Empathic Virtual Agent Delivers Behavior Change Health Interventions
ACM Transactions on Management Information Systems (TMIS) - Special Issue on Informatics for Smart Health and Wellbeing
Integration of multi-feature fusion and dictionary learning for face recognition
Image and Vision Computing
Face recognition for web-scale datasets
Computer Vision and Image Understanding
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Computer vision systems have demonstrated considerable improvement in recognizing and verifying faces in digital images. Still, recognizing faces appearing in unconstrained, natural conditions remains a challenging task. In this paper, we present a face-image, pair-matching approach primarily developed and tested on the “Labeled Faces in the Wild” (LFW) benchmark that reflects the challenges of face recognition from unconstrained images. The approach we propose makes the following contributions. 1) We present a family of novel face-image descriptors designed to capture statistics of local patch similarities. 2) We demonstrate how unlabeled background samples may be used to better evaluate image similarities. To this end, we describe a number of novel, effective similarity measures. 3) We show how labeled background samples, when available, may further improve classification performance, by employing a unique pair-matching pipeline. We present state-of-the-art results on the LFW pair-matching benchmarks. In addition, we show our system to be well suited for multilabel face classification (recognition) problem, on both the LFW images and on images from the laboratory controlled multi-PIE database.