۱٫ Introduction

۲٫ Related works

۳٫ Proposed method

۴٫ Experimental results and analysis

۵٫ Discussions

۶٫ Conclusion and recommendations for future work

Acknowledgement

References

Abstract

Kernel-based Sparse Representation (SR) has impacted positively on the classification performance in image recognition and has eradicated the problems attributed to the nonlinear distribution of face images and its implementation with Dictionary Learning (DL). However, the locality construction of image data containing more discriminative information, which is crucial for classification has not been fully examined by the current Kernel Sparse representation-based approaches. Furthermore, similar coding outcomes between test samples and neighbouring training data, restrained in the kernel space are not being fully realized from the image features with similar image groupings to effectively capture the embedded discriminative information. To handle these issues, we propose a novel DL method, Kernel Locality-Sensitive Discriminative SR (K-LSDSR) for face recognition. In the proposed K-LSDSR, a discriminative loss function for the groupings based on sparse coefficients is introduced into a locality-sensitive DL (LSDL). After solving the optimized dictionary, the sparse coefficients for the testing image feature samples are obtained, and then the classification results for face recognition is realized by reducing the error between the original and reassembled samples. Experimental results have shown that the proposed K-LSDSR significantly improves the performance of face recognition accuracies compared with competing methods and is robust to various diverse environments in image recognition.

Introduction

SR is mostly applied to signal reconstruction [55], experiments have also shown that it performs well with video analysis and image classification [41,52]. Again, the implementation of SR for face recognition in the past decades has received a lot of attention in the area of pattern recognition and Computer Vision. For a specified signal to be thoroughly represented, a good dictionary must be learned from training samples. This shows that the quality of the dictionary is very crucial for efficient SR. The dictionary could be realized either by utilizing all the training samples as the dictionary to code the test samples, as in the case of the locality constrained linear coding [38] or a learned dictionary for SR is adopted for each training sample in the set (e.g. K-means, Singular Value Decomposition (K-SVD) algorithm, Fisher Discriminative Dictionary Learning (FDDL)). Training samples are utilized as the dictionary for all the methods that adopts the first strategy. Even though, they exhibit good performance with regards to classification, the dictionary might not be that effective to exemplify the samples well. This is mainly attributed to noisy information that may have accompanied the initial training samples and may not fully capture the Discrimination information embedded in the training samples. The latter grouping is also not appropriate for recognition, since it only ensures that the dictionary is best expressed in the training samples with strict SR. Even though numerous approaches including the LSDL [27,41,43] that incorporated a Locality Regularization Constraint into the structure of the DL which guarantees that the over complete dictionary (OCD) Leaned is representative enough to resolve the above stated issues, there was still the need for methods that will help resolve issues of noise, occlusion, aging, variations in resolution, different facial expressions of objects, and illumination changes in universal environments.