Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Journal of Cognitive Neuroscience
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Photo search by face positions and facial attributes on touch devices
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Where is who: large-scale photo retrieval by facial attributes and canvas layout
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
PDSS: patch-descriptor-similarity space for effective face verification
Proceedings of the 20th ACM international conference on Multimedia
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We aim to develop a scalable face image retrieval system which can integrate with partial identity information to improve the retrieval result. To achieve this goal, we first apply sparse coding on local features extracted from face images combining with inverted indexing to construct an efficient and scalable face retrieval system. We then propose a novel coding scheme that refines the representation of the original sparse coding by using identity information. Using the proposed coding scheme, face images with large intra-class variances will still be quantized into similar visual words if they share the same identity. Experimental results show that our system can achieve salient retrieval results on LFW dataset (13K faces) and outperform linear search methods using well known face recognition feature descriptors.