Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Towards context-aware face recognition
Proceedings of the 13th annual ACM international conference on Multimedia
A Mobile Vision System for Urban Detection with Informative Local Descriptors
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Adaptive training of video sets for image recognition on mobile phones
Personal and Ubiquitous Computing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
TagSense: a smartphone-based approach to automatic image tagging
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Balancing energy, latency and accuracy for mobile sensor data classification
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
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Face recognition has many challenges. For instance, the illumination, various facial expression and different viewpoints add difficulties to identify the same person from a bunch of images. Searching over a huge set of images will only amplify such difficulties. We introduce the location aware face recognition framework for mobile-taken photos to alleviate the hardness. With the help of location sensor on the mobile devices, we collect images with location information. We propose an algorithm to reduce the search space of face recognition and therefore achieve better accuracy. Photos are clustered by locations on the server. Each location is then associated with a face classifier. Every client can send a "Who is Here" type query to the server by uploading an image with the location. The algorithm on the server will search over the given location and identify the person on the image. Experiments are conducted on mobile devices. The results are quite promising that higher accuracy is achieved and the query can be answered in near real-time.