This paper proposes a robust face recognition method based on sparse representation. The key idea is to represent a test sample as a sparse linear combination of training samples from the same class. This approach leverages the theory of sparse signal representation to address the challenges of face recognition, including feature extraction and robustness to occlusion and corruption. The method uses $ \ell^{1} $-minimization to compute the sparse representation, which allows for efficient and accurate classification. The framework is shown to be effective in handling errors due to occlusion and corruption by exploiting the sparsity of these errors with respect to the standard pixel basis. The method is validated on publicly available databases and is shown to outperform traditional methods in terms of recognition accuracy and robustness. The paper also discusses the role of feature extraction, demonstrating that the choice of features is less critical when using sparse representation, as long as the feature space is sufficiently large. The method is robust to occlusion and corruption, as it can handle errors that are sparse in the standard pixel basis. The paper concludes that the proposed framework provides a powerful tool for face recognition, with applications in a wide range of image-based object recognition tasks.This paper proposes a robust face recognition method based on sparse representation. The key idea is to represent a test sample as a sparse linear combination of training samples from the same class. This approach leverages the theory of sparse signal representation to address the challenges of face recognition, including feature extraction and robustness to occlusion and corruption. The method uses $ \ell^{1} $-minimization to compute the sparse representation, which allows for efficient and accurate classification. The framework is shown to be effective in handling errors due to occlusion and corruption by exploiting the sparsity of these errors with respect to the standard pixel basis. The method is validated on publicly available databases and is shown to outperform traditional methods in terms of recognition accuracy and robustness. The paper also discusses the role of feature extraction, demonstrating that the choice of features is less critical when using sparse representation, as long as the feature space is sufficiently large. The method is robust to occlusion and corruption, as it can handle errors that are sparse in the standard pixel basis. The paper concludes that the proposed framework provides a powerful tool for face recognition, with applications in a wide range of image-based object recognition tasks.