Abstract
In recent years, dictionary learning has received more and more attention in the study of face recognition. However, most dictionary learning algorithms directly use the original training samples to learn the dictionary, ignoring noise existing in the training samples. For example, there are differences between different images of the same subject due to changes in illumination, expression, etc. To address the above problems, this paper proposes the dictionary relearning algorithm (DRLA) based on locality constraint and label embedding, which can effectively reduce the influence of noise on the dictionary learning algorithm. In our proposed dictionary learning algorithm, first, the initial dictionary and coding coefficient matrix are directly obtained from the training samples, and then the original training samples are reconstructed by the product of the initial dictionary and coding coefficient matrix. Finally, the dictionary learning algorithm is reapplied to obtain a new dictionary and coding coefficient matrix, and the newly obtained dictionary and coding coefficient matrix are used for subsequent image classification. The dictionary reconstruction method can partially eliminate noise in the original training samples. Therefore, the proposed algorithm can obtain more robust classification results. The experimental results demonstrate that the proposed algorithm performs better in recognition accuracy than some state-of-the-art algorithms.
Highlights
In recent years, dictionary learning has been widely applied in various fields due to its excellent performance, such as face recognition [1,2,3], image denoising [4, 5] and blurring [6, 7], image segmentation [8, 9], and image recognition [10]
For face recognition [11, 12], the conventional dictionary learning method first learns a dictionary through the training samples. en, given a test image, the image is represented by the atoms in the dictionary
According to previous studies, using a dictionary obtained by the training samples to represent and classify test samples can lead to a higher accuracy than directly using training samples to represent and classify the test samples [13,14,15]. e dictionary learning method has achieved very significant performance in face recognition applications so that researchers have proposed various dictionary learning methods
Summary
Dictionary learning has been widely applied in various fields due to its excellent performance, such as face recognition [1,2,3], image denoising [4, 5] and blurring [6, 7], image segmentation [8, 9], and image recognition [10]. For face recognition [11, 12], the conventional dictionary learning method first learns a dictionary through the training samples. In the field of sparse coding and dictionary learning, the use of the local structure of data is considered to be an important way to improve performance. Haghiri et al [26] proposed a discriminative dictionary learning method that preserves the local structure of the training samples. Erefore, this paper proposes the dictionary relearning algorithm (DRLA) based on LocalityConstrained and Label Embedding Dictionary Learning Algorithm (LCLE-DL), which can partially eliminate noise in the original image. The dictionary learning algorithm uses the updated training samples matrix to obtain the reconstructed dictionary and coding coefficient matrix, and the reconstructed result is used for subsequent image classification.
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