References in periodicals archive ?
At last, we show the superior performance of the LRCR method for facial gender recognition under an unconstrained environment on comparison with 6 the state-of-art algorithms.
We compared the proposed LRCR method against other 7 state-of-the-art methods which including LBP+SVM, 2D-Gabor+SVM, LBP+Adaboost, 2D-Gabor+Adaboost, CRC without low-rank decomposition, DBN and CNN.
Primarily, it can be clearly seen that the proposed LRCR algorithm is remarkably superior to the other 6 methods in almost all possible input image size.
The recognition accuracy of another deep neural networks model--DBN is obviously lower than that of CNN and the LRCR.
LRCR is the most time-consuming method because of its complex computational processing.
All in all, compared with the other the-state-of-the-art methods, the LRCR method can effectively identify the gender from the nature face images captured in the unconstrained environment.
In this paper, the LRCR method is proposed to recognize the gender from nature face images in the unconstraint environment.