Classification of Domestic Dogs Emotional Behavior Using Computer Vision

Víctor Ocyel Chavez-Guerrero, Humberto Perez-Espinosa, María Eugenia Puga-Nathal, Veronica Reyes-Meza


Dogs are the most common companion animals worldwide, motivated by their exceptional social behavior with humans. Unlike many animals, dog learn vocal commands, identify moods, maintain eye contact, and recognize facial expressions. Besides, dogs have great agility and senses of smell and hearing superior to humans, so dogs have been trained for crucial tasks like search, rescue, and assistance. Therefore, it is relevant to do scientific research to understand the fundamentals of behavior and communication that increase the use of its capabilities for the benefit of the human being, guaranteeing the animal’s welfare. In this work, a computational method for analyzing dog behavior based on artificial vision techniques was developed. A video database recorded in positive and negative stimuli that induced different emotional states was used. The proposed method determines the dog’s emotional state at a given time, which opens a promising field to develop new technologies that trainers and users can take advantage of, to improve the processes of selection, training, and execution of the tasks of working dogs. Using the proposed method, the best test accuracy value we obtained was 0.6917 on the best model trained using transfer learning over the architecture MobileNet, getting good but not perfect results. The training process was carried out using 1067 images distributed among four categories, aggressiveness, anxiety, fear and neutral. The proposed method obtained acceptable results but can still be improved in technical and methodology terms. However, this method can be used as a baseline for exploring and expanding the canine behavior study using computational models.


Computer vision, machine learning, canine behavior

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