21,760 research outputs found
Feature selection method based on sparse representation classification for face recognition
Compressed sensing is a signal processing technique.
The entity signal can be efficiently reconstructed if the sparse representation is determined. The sparse representations of all the test images are determined with respect to the training set by computing the l1-minimization. However, sparse representation which involves high dimensional feature vector is computationally expensive. Thus, discriminative features that could perform accurately for the face recognition system under visual variations, such as illumination, expression and occlusion have to be selected carefully. In this paper, feature selection method in the application of face recognition based on sparse representation classifier (SRC) is proposed. The proposed technique first divides the images of a few subjects into chunks. Then, it selects the feature subsets based on distance based measurement, the residual, and recognition performance, the accuracy. Extensive experiments with visual variations are carried out by using ORL, AR and Yale databases
Multiple pattern classification by sparse subspace decomposition
A robust classification method is developed on the basis of sparse subspace
decomposition. This method tries to decompose a mixture of subspaces of
unlabeled data (queries) into class subspaces as few as possible. Each query is
classified into the class whose subspace significantly contributes to the
decomposed subspace. Multiple queries from different classes can be
simultaneously classified into their respective classes. A practical greedy
algorithm of the sparse subspace decomposition is designed for the
classification. The present method achieves high recognition rate and robust
performance exploiting joint sparsity.Comment: 8 pages, 3 figures, 2nd IEEE International Workshop on Subspace
Methods, Workshop Proceedings of ICCV 200
Wireless Interference Identification with Convolutional Neural Networks
The steadily growing use of license-free frequency bands requires reliable
coexistence management for deterministic medium utilization. For interference
mitigation, proper wireless interference identification (WII) is essential. In
this work we propose the first WII approach based upon deep convolutional
neural networks (CNNs). The CNN naively learns its features through
self-optimization during an extensive data-driven GPU-based training process.
We propose a CNN example which is based upon sensing snapshots with a limited
duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs
between 15 classes. They represent packet transmissions of IEEE 802.11 b/g,
IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the
2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII
approaches and has a classification accuracy greater than 95% for
signal-to-noise ratio of at least -5 dB
Face Detection & Recognition based on Fusion of Omnidirectional & PTZ Vision Sensors and Heteregenous Database
International audienceLarge field of view with high resolution has always been sought-after for Mobile Robotic Authentication. So the Vision System proposed here is composed of a catadioptric sensor for full range monitoring and a Pan Tilt Zoom (PTZ) camera together forming an innovative sensor, able to detect and track any moving objects at a higher zoom level. In our application, the catadioptric sensor is calibrated and used to detect and track Regions Of Iinterest (ROIs) within its 360 degree Field Of View (FOV), especially face regions. Using a joint calibration strategy, the PTZ camera parameters are automatically adjusted by the system in order to detect and track the face ROI within a higher resolution and project the same in faces-pace for recognition via Eigenface algorithm. Face recognition is one important task in Nomad Biometric Authentication (NOBA 1) project. However, as many other face databases, it will easily produce the Small Sample Size (SSS) problem in some applications with NOBA data. Thus this journal uses the compressed sensing (CS) algorithm to solve the SSS problem in NOBA face database. Some experiments can prove the feasibility and validity of this solution. The whole development has been partially validated by application to the Face recognition using our own database NOBA
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