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Title: Hidden and Face-Like Object Detection Using Deep Learning Techniques - An Empirical Study
Contributor(s): A, Anirudh (author); Chakraborty, Subrata  (author)orcid 
Publication Date: 2022
Publisher: IEEE
DOI: 10.1109/DICTA56598.2022.10034632
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Source of Publication: Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA), p. 1-8

An essential aspect of artificial intelligence is how closely machines can mimic humans. One of the motivations for developing intelligent systems is human vision. While trying to recognise a class of images, it is as vital to distinguish the class of images from similar-looking objects and identify them in hidden places as it is to create bounding boxes and learn to localize the position of the object. Traditionally, deep learning models have performed exceptionally well in image classification and object detection tasks. In this work, we perform four experiments to train machines to distinguish between real faces and face-like objects and to recognise them. Nine state-of-the-art deep learning-based classifiers have been chosen to perform a comparative study on the designed experiments. Using these experiments, we establish that training models on real faces does not prepare them to identify face-like objects, and at the same time, training on face-like objects enables the models to detect face-like images even while hidden amongst other images. Despite work being done in the fields of camouflage detection and optical illusion detection, to the best of our knowledge, no work has been done in training and testing machines to distinguish between face and face-like objects with deep learning methods. This work could help researchers make better camouflage detection systems, perform context sensitive studies, understand the biases that various models possess towards certain classes of images, and have applications in real life such as military and self-driving cars.

Publication Type: Conference Publication
Fields of Research (FoR) 2020: 4601 Applied computing
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Peer Reviewed: Yes
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