access icon free Animal classification using facial images with score-level fusion

© The Institution of Engineering and Technology
Received 31/01/2017, Accepted 01/03/2018, Revised 11/01/2018, Published 02/03/2018

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.

Inspec keywords: image representation; biometrics (access control); face recognition; computer vision; object recognition; neural nets; image classification; feature extraction; pattern classification; learning (artificial intelligence)

Other keywords: simple feature extraction techniques; simple features; animal faces; appearance-based descriptor features; score-level fusion; computer vision approaches; animal life; feature extraction phase; image classification; classification accuracy; uses CNN; appropriate score-level combination; facial images; recently popular convolutional neural network features; real-world animal biometric system; automatic feature extraction; animal face classification; 95.31% classification rate; animal classification; video data

Subjects: Neural computing techniques; Knowledge engineering techniques; Image recognition; Other topics in statistics; Optical, image and video signal processing; Computer vision and image processing techniques

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