Abstract
In this paper, we propose a multistage KNN collaborative coding based Bag-of-Feature (MKCC-BoF) method to address SSPP problem, which tries to weaken the semantic gap between facial features and facial identification. First, local descriptors are extracted from the single training face images and a visual dictionary is obtained offline by clustering a large set of descriptors with K-means. Then, we design a multistage KNN collaborative coding scheme to project local features into the semantic space, which is much more efficient than the most commonly used non-negative sparse coding algorithm in face recognition. To describe the spatial information as well as reduce the feature dimension, the encoded features are then pooled on spatial pyramid cells by max-pooling, which generates a histogram of visual words to represent a face image. Finally, a SVM classifier based on linear kernel is trained with the concatenated features from pooling results. Experimental results on three public face databases show that the proposed MKCC-BoF is much superior to those specially designed methods for SSPP problem. Moreover, it also has great robustness to expression, illumination, occlusion and, time variation.
Highlights
In the past decades, face recognition has been paid more and more attention due to its great application prospect in the fields of public safety [38], transportation [36], finance [20], social media [29, 30] and so on
Experimental results on three public face databases show that the proposed multistage KNN collaborative coding (MKCC)-BoF generates well to single sample per person (SSPP) problem and has great robustness to expression, illumination, occlusion and, time variation
MKCC-BoF is still superior to negative sparse coding (NSC)-BoF, which demonstrates the advantages of the proposed MKCC scheme once again
Summary
Face recognition has been paid more and more attention due to its great application prospect in the fields of public safety [38], transportation [36], finance [20], social media [29, 30] and so on. For effective and efficient global methods, subspace learning methods such as PCA [19] and FLDA [4] are adopted, which have achieved impressive results in face recognition applications They are affected by those regions with variances in illumination, expression, occlusion etc. In many real-world applications such as identity card verification, passport verification in customs, law enforcement, surveillance or access control, only one training sample per person is available This is so called single sample per person (SSPP) problem [32] which has become one of the greatest challenges in face recognition. Many conventional global or local methods will suffer serious performance drop or fail to work when encountering SSPP problem This is mainly because it is difficult to distinguish the image changes caused by illumination, expression, occlusion etc.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
