NDLI: Animal classification using facial images with score-level fusion

Content Provider	IET Digital Library
Author	Taheri, Shahram Toygar, Önsen
Abstract	A real-world animal biometric system that detects and describes animal life in image and video data is an emerging subject in machine vision. These systems develop computer vision approaches for the classification of animals. A novel method for animal face classification based on score-level fusion of recently popular convolutional neural network (CNN) features and appearance-based descriptor features is presented. This method utilises a score-level fusion of two different approaches; one uses CNN which can automatically extract features, learn and classify them; and the other one uses kernel Fisher analysis (KFA) for its feature extraction phase. The proposed method may also be used in other areas of image classification and object recognition. The experimental results show that automatic feature extraction in CNN is better than other simple feature extraction techniques (both local- and appearance-based features), and additionally, appropriate score-level combination of CNN and simple features can achieve even higher accuracy than applying CNN alone. The authors showed that the score-level fusion of CNN extracted features and appearance-based KFA method have a positive effect on classification accuracy. The proposed method achieves 95.31% classification rate on animal faces which is significantly better than the other state-of-the-art methods.
Starting Page	679
Ending Page	685
Page Count	7
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 5, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0079
Journal	IET Computer Vision
Publisher Date	2018-03-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	95.31% Classification Rate Animal Classification Animal Face Classification Animal Faces Animal Life Appearance-based Descriptor Features Appropriate Score-level Combination Automatic Feature Extraction Biomimetics Classification Accuracy Computer Vision Computer Vision And Image Processing Technique Computer Vision Approach Face Recognition Facial Image Feature Extraction Feature Extraction Phase Image Classification Image Recognition Image Representation Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Nets Object Recognition Optical, Image And Video Signal Processing Pattern Classification Real-world Animal Biometric System Recently Popular Convolutional Neural Network Feature Score-level Fusion Simple Feature Simple Feature Extraction Technique Statistics Uses CNN Video Data
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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