Abstract
The sparse representation-based classification (SRC) has been proven to be a robust face recognition method. However, its computational complexity is very high due to solving a complex -minimization problem. To improve the calculation efficiency, we propose a novel face recognition method, called sparse representation-based classification on k-nearest subspace (SRC-KNS). Our method first exploits the distance between the test image and the subspace of each individual class to determine the nearest subspaces and then performs SRC on the selected classes. Actually, SRC-KNS is able to reduce the scale of the sparse representation problem greatly and the computation to determine the nearest subspaces is quite simple. Therefore, SRC-KNS has a much lower computational complexity than the original SRC. In order to well recognize the occluded face images, we propose the modular SRC-KNS. For this modular method, face images are partitioned into a number of blocks first and then we propose an indicator to remove the contaminated blocks and choose the nearest subspaces. Finally, SRC is used to classify the occluded test sample in the new feature space. Compared to the approach used in the original SRC work, our modular SRC-KNS can greatly reduce the computational load. A number of face recognition experiments show that our methods have five times speed-up at least compared to the original SRC, while achieving comparable or even better recognition rates.
Highlights
In recent years, face recognition has been, and remains being, one of the hottest and most challenging research topics in computer vision, pattern recognition and biometrics
We tested sparse representation-based classification (SRC)-KNS on three databases with different scales under normal conditions to verify the efficiency despite the variation of database size
Each database varies in the number of subjects, our method achieves 5 times speed-up at least compared to the original SRC when the selected nearest subspaces is less than 10% of total subjects
Summary
Face recognition has been, and remains being, one of the hottest and most challenging research topics in computer vision, pattern recognition and biometrics. There are a large number of face recognition methods, as well as their modifications. They can be categorized into two basic classes, i.e. model-based and appearance-based methods [1]. In appearance-based methods, an a|b face image can be represented by a vector of ab-dimension. A large number of linear and non-linear transform methods have been widely used at the feature extraction stage to transform images from original image space into a new low-dimensional feature space. Typical examples of linear transform methods include Principle Component Analysis (PCA) [2,3], Linear Discriminant Analysis (LDA) [4], and Independent Component Analysis (ICA) [5,6,7]. While kernel PCA [8] and kernel LDA [9] are two widely used nonlinear transform methods
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