A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques

Recently, visual animal bio-metrics have attracted much attention for identifying endangered species and animals based on their prominent bio-metric features. This study explores a new possibility of using facial recognition for an inexpensive and user-friendly bio-metric modality to identify cattle...

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Veröffentlicht in:Multimedia tools and applications Jg. 81; H. 21; S. 31075 - 31106
Hauptverfasser: Kumar, Santosh, Kumar, Sunil, Shafi, Mehak, Chaube, Mithilesh Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2022
Springer Nature B.V
Schlagworte:
Belief networks
Cattle
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Endangered species
Face recognition
Feature extraction
Multimedia Information Systems
Noise reduction
Object recognition
Representations
Special Purpose and Application-Based Systems
Visual animal bio-metrics
Deep learning
SDAE
Group sparse representation
Muzzle point
ISSN:1380-7501, 1573-7721
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Zusammenfassung:Recently, visual animal bio-metrics have attracted much attention for identifying endangered species and animals based on their prominent bio-metric features. This study explores a new possibility of using facial recognition for an inexpensive and user-friendly bio-metric modality to identify cattle. This paper proposes an automatic recognition system to identify cattle based on face and muzzle image features using sparse Stacked Denoising Autoencoder (SDAE) and group sparse representation techniques. The discriminatory features are extracted from cattle’s facial image and muzzle images using sparse SDAE and Deep Belief Networks (DBN) methods to represent extracted features. It mitigates the need for individual bio-metric feature representation from the single face and muzzle bio-metric modality features at various score levels. The single modality features and multi-features are fused at score level to represent better and classify individual cattle using group sparse representation technique. The proposed system’s performance is compared with holistic and handcrafted texture feature extraction and representation technique and current state of the art methods. Author shows that the proposed recognition system yield 96.85% accuracy for identifying individual cattle based on the multi-feature-based representation of cattle’s face and muzzle features.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-12701-w