An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Face Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Illumination Invariant Face Recognition Using Near-Infrared Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cognitive face analysis system for future interactive TV
IEEE Transactions on Consumer Electronics
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In this paper, a face recognition system which can be applied to an interactive smart TV Control System is proposed. Face Recognition system using near-infrared (NIR) face images is developed because NIR images can be captured in a somewhat dark environment. The face recognition system consists of three subsystems. The first is for the registration of the user's face and the second is for the detection of the user's face. The final subsystem is for the recognition of the user via the user's face. The face recognition system is used with a interactive smart TV in order to provide personalized services such as the selection of favorite channels or parental guidance. To detect a face, we extract Uniform Local Binary Patterns (ULBP) histogram features in NIR face images and use Support Vector Machine (SVM) as a classifier. To recognize a face, we extract local Gabor binary pattern histogram sequences (LGBPHS) and compare faces using a chi-square distance measure. The experiments show the global recognition accuracy is about 97% by using our NIR face database.