To improve the discriminative ability of the coding coefficients, the Profiles (the line vectors of coding coefficients matrix) of Fisher discriminative dictionary learning (PFDDL) is proposed. Firstly, the Profiles can indicate the corresponding atoms which are used by the training samples to encode in the dictionary learning, and an adaptive method is proposed to construct the labels of atoms. Since there are one-to-one correspondences between the Profiles and atoms, then the Fisher discriminative criterion is imposed on the Profiles so that they have small within-class compactness but large between-class separability. Thus, it can encourage the atoms of the same class to reconstruct the training sample of the same class, and enhance the discriminative ability of the coding coefficients, then improve the performance of dictionary learning. Experimental results show that the PFDDL algorithm can achieve better classification performance than other sparse coding and dictionary learning algorithms on the three face and one handwriting databases.