Combined Approach of Supervised and Unsupervised learning for Dog Face Recognition

Abstract

One would be surprised to hear the lost dog rates around the world. Even though it is something that one doesn't ponder a lot about, lost dogs are a problem that most dog owners fear. Dogs provide humans with companionship, protection, and unconditional love, and to the dogs; their whole world revolves around their owner and their family members. Therefore, when a pet dog goes missing, not only the dog owner but also the pet dog is affected. Unfortunately, in Sri Lanka, a lost dog being found is a very rare occurrence. A reason for this can be pointed out as the lack of an easily-accessible, public platform for lost dogs. In this research project, a solution to this problem has been implemented using image processing. This research study is about image classification and recognition using the Convolutional Neural Network (CNN) or also known as Shift Invariant or Space Invariant Artificial Neural Network (SIANN) by using TensorFlow framework as well as Keras library. The VGG16 model was customized for being used feature extraction. The implementation was a combination of both Machine Learning and Deep Learning. The platform to upload the found dog is also a continuous and inter-related subcomponent that provides a happy and healthy life for stray dogs too. That idea is providing them a higher chance to find a safe place to survive and also a home where they will be loved. The results are discussed in terms of the accuracy of the image recognition and classification in percentage. Each group of dogs get around 90% accuracy or above.