Animal classification using facial images with score-level fusion - Taheri, Shahram; Toygar, Onsen (Wiley, 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.
Keywords: image classification; pattern classification; face recognition; computer vision; feature extraction; neural nets; image representation; biometrics (access control); learning (artificial intelligence); object recognition; animal classification; facial images; score-level fusion; real-world animal biometric system; animal life; video data; computer vision approaches; animal face classification; recently popular convolutional neural network features; appearance-based descriptor features; uses CNN; feature extraction phase; image classification; automatic feature extraction; simple feature extraction techniques; appropriate score-level combination; simple features; classification accuracy; 95; 31% classification rate; animal faces