Brain Inspired Object Recognition System
- URL: http://arxiv.org/abs/2105.07237v1
- Date: Sat, 15 May 2021 14:42:17 GMT
- Title: Brain Inspired Object Recognition System
- Authors: Pinaki Roy Chowdhury, Angad Wadhwa, Antariksha Kar and Nikhil Tyagi
- Abstract summary: Histogram of Oriented Gradients, Local Binary Patterns, and Principal components extracted from target images are used.
A computational theory is first developed by using concepts from the information processing mechanism of the brain.
Experiments are carried out using fifteen publicly available datasets to validate the performance of our proposed model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This paper presents a new proposal of an efficient computational model of
face and object recognition which uses cues from the distributed face and
object recognition mechanism of the brain, and by gathering engineering
equivalent of these cues from existing literature. Three distinct and widely
used features, Histogram of Oriented Gradients, Local Binary Patterns, and
Principal components extracted from target images are used in a manner which is
simple, and yet effective. Our model uses multi-layer perceptrons (MLP) to
classify these three features and fuse them at the decision level using sum
rule. A computational theory is first developed by using concepts from the
information processing mechanism of the brain. Extensive experiments are
carried out using fifteen publicly available datasets to validate the
performance of our proposed model in recognizing faces and objects with extreme
variation of illumination, pose angle, expression, and background. Results
obtained are extremely promising when compared with other face and object
recognition algorithms including CNN and deep learning based methods. This
highlights that simple computational processes, if clubbed properly, can
produce competing performance with best algorithms.
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