Open AccessResearch on pig face recognition model based on keras convolutional neural network
Author(s) -
Ke Wang,
Changxi Chen,
Yong He
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/474/3/032030
Subject(s) - dropout (neural networks) , convolutional neural network , artificial intelligence , computer science , pattern recognition (psychology) , deep learning , feature (linguistics) , face (sociological concept) , facial recognition system , artificial neural network , speech recognition , machine learning , social science , philosophy , linguistics , sociology
Traditionally, the RFID technology is generally used for the identification of pigs in vivo. However, the method of electronic ear tags and ear tags will cause great pain to the pigs, then ear tags will easily fall off during the pig’s activities, increasing the operating cost of the enterprise. This paper uses the powerful feature learning and feature expression capabilities of convolutional neural networks in deep learning to automatically learn the facial features of pigs. Use the Image Data Generator that comes with Keras to perform data enhancement on the pig face pictures of ten pigs and generate pig face dataset. This paper proposes a convolutional neural network model based on LeNet-5 for facial image recognition of pigs. Experimental comparisons were performed by using SGD, Adam and rmsprop optimizers with dropout ratios of 0.3, 0.5and 0.7. Experiments show that when the SGD optimizer is used and dropout is 0.3, the model recognition rate is the highest, which can reach 97.6%.