TY - JOUR
AU - Plataniotis, Konstantinos
AB - This paper proposes a novel face recognition algorithm that utilizes a sparse Fast Fourier Transform (FFT)-based feature extraction method. In our algorithm, we use Compressive Sampling (CS) theory two times. First, in the feature extraction process for extracting the feature vectors from a face images, and second, in the classification process where the CS reconstruction is used for selecting true classes. As a result, a significant reduction in the dimensionality of the signals is achieved. Extensive and comparative experiments have been conducted to evaluate the performance of the proposed scheme. The experiment results show that the combined Compressive Sensing and Sparse Representation Classification (SRC) achieves a high recognition accuracy, while maintaining a reasonable computational complexity.
TI - Face recognition using a new compressive sensing-based feature extraction method
JF - Multimedia Tools and Applications
DO - 10.1007/s11042-017-5007-0
DA - 2017-07-10
UR - https://www.deepdyve.com/lp/springer-journals/face-recognition-using-a-new-compressive-sensing-based-feature-ev0O6fahTS
SP - 14007
EP - 14027
VL - 77
IS - 11
DP - DeepDyve
ER -