More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition
Algorithm 1
Online sparse learning algorithm.
Initialization:
(1)
Obtain gallery set , testing set , initial salience evaluation vector , and facial feature description model .
(2)
Set the initial value for the number of iterations: n = 1.
(3)
Set the initial value for the sign vector (each element denotes whether the label of the testing sample is correct, “1” denotes “right” and “−1” denotes “wrong”) SV = 1.
(4)
Randomly select a testing sample from .
Iteration:
(5)
while {the number of the consecutive testing sample labeled incorrectly: k < 5} do:
(6)
.
(7)
Compute the facial feature vector for with equation (1).
(8)
Compute the label for with ,, and the nearest neighbor model.
(9)
Judge the correctness of according the criterion in Section 3.2: if is correct, then update according the gallery set update strategy in Section 3.3; if is incorrect, then repeat steps (4)∼(8).
(10)
Construct the nearest neighbor set and next-nearest neighbor set : find the nearest neighbor samples of from , and all the nearest neighbor samples form set ; similarly, the next-nearest neighbor set is obtained.
(11)
Construct positive and negative sample set and according to equation (7).
(12)
Solve the optimization problem equations (9)∼(10) to obtain .
(13)
Compute the salience evaluation vector β with equation (4).
