Impact of Kernel-PCA on Different Features for Person Re-Identification
Md Kamal Uddin1, Amran Bhuiyan2, Mahmudul Hasan3
1Md Kamal Uddin*, Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh.
2Amran Bhuiyan, Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh.
3Mahmudul Hasan, Department. of Computer Science and Engineering, Comilla University, Comilla, Bangladesh.
Manuscript received on August 26, 2021. | Revised Manuscript received on September 20, 2021. | Manuscript published on September 30, 2021. | PP: 76-81 | Volume-10 Issue-11, September 2021. | Retrieval Number: 100.1/ijitee.K945709101121 | DOI: 10.35940/ijitee.K9457.09101121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the driving field of computer vision, re-identification of an individual in a camera network is very challenging task. Existing methods mainly focus on strategies based on feature learning, which provide feature space and force the same person to be closer than separate individuals. These methods rely to a large extent on high-dimensional feature vectors to achieve high re-identification accuracy. Due to computational cost and efficiency, they are difficult to achieve in practical applications. We comprehensively analyzed the effect of kernel-based principal component analysis (PCA) on some existing high-dimensional person re-identification feature extractors to solve these problems. We initially formulate a kernel function on the extracted features and then apply PCA, significantly reducing the feature dimension. After that, we have proved that the kernel is very effective on different state-of-the-art high-dimensional feature descriptors. Finally, a thorough experimental evaluation of the reference person re-identification data set determined that the prediction method was significantly superior to more advanced techniques and computationally feasible.
Keywords: Feature Dimension, Kernel-PCA, Re-identification, Video surveillance.