Abstract
Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification. As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition. The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10% improvement in recognition accuracy.
Highlights
Feature extraction and classification are two key steps in face recognition [1, 2]
We conducted comprehensive experiments on several mainstream benchmark face databases to compare the robustness of our WASCRC and conventional Sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) algorithms
In order to further understand the cause of these improvements, we added a step to analyze the change in representation coefficients in all three algorithms
Summary
Extraction of features is the basic of mathematical calculation performed in classification methods. One prevailing paradigm is to use statistical learning approaches based on training data to determine proper features to extract and how to construct classification engines. Representation-based classification methods (RBCM), such as PCA [3, 4] and LDA [5, 6], have significantly improved face recognition techniques. The basic process in these methods is as follows: first all training samples are coded to obtain a representation matrix, this matrix is used to evaluate each test sample and determine new lower-dimensional representation coefficients, and classification is performed based on these coefficients [2, 9]. The robustness of face recognition is determined by suitability of the representation coefficients
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