A Survey of Dictionary Learning Algorithms for Face Recognition
TLDR
A survey of dictionary learning algorithms for face recognition is provided to understand the profiles of this subject and to grasp the theoretical rationales and potentials as well as their applicability to different cases of face recognition.Abstract:
During the past several years, as one of the most successful applications of sparse coding and dictionary learning, dictionary-based face recognition has received significant attention. Although some surveys of sparse coding and dictionary learning have been reported, there is no specialized survey concerning dictionary learning algorithms for face recognition. This paper provides a survey of dictionary learning algorithms for face recognition. To provide a comprehensive overview, we not only categorize existing dictionary learning algorithms for face recognition but also present details of each category. Since the number of atoms has an important impact on classification performance, we also review the algorithms for selecting the number of atoms. Specifically, we select six typical dictionary learning algorithms with different numbers of atoms to perform experiments on face databases. In summary, this paper provides a broad view of dictionary learning algorithms for face recognition and advances study in this field. It is very useful for readers to understand the profiles of this subject and to grasp the theoretical rationales and potentials as well as their applicability to different cases of face recognition.read more
Citations
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Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition
TL;DR: The proposed DFEDL algorithm achieves superior performance in comparison with some state-of-the-art dictionary learning algorithms on both hand-crafted and deep learning-based features.
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Low-dose spectral CT reconstruction using image gradient ℓ0-norm and tensor dictionary.
TL;DR: The results show that the proposed ℓ 0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
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Face recognition: Past, present and future (a review)
TL;DR: The methods used to obtain and classify facial biometric data in the literature have been summarized and a taxonomy of image-based and video-based face recognition methods is given, outlining the major historical developments, and the main processing steps.
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Locality constrained representation-based K-nearest neighbor classification
TL;DR: Two locality constrained representation-based k-nearest neighbor rules are proposed with the purpose of further improving the KNN-based classification performance and performing better with less sensitiveness to k, especially in the small sample size cases.
References
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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.
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