Cat Breed & Emotion Detection Using Yolo, CNN & Canny Edge Detection

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@article{1705454,
      author = {Kunika Jangid, Mithilesh Vishwakarma, Kiran Pal, Lisa Rodrigues},
      title = {Cat Breed & Emotion Detection Using Yolo, CNN & Canny Edge Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {8},
      pages = {1-9},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705454.pdf},
      abstract = {Automatic cat facial expression recognition is actively emerging research in This study explores the recognition of emotional states in cats through their facial expressions, drawing inspiration from the extensive research conducted on human facial expressions. We propose a hybrid approach utilizing Convolutional Neural Networks (CNNs) and Canny edge detection to identify and classify cat facial emotions. To mitigate overfitting in the CNN model, we employ a regularization technique known as "dropout" in the fully connected layers. Additionally, we extend our system's capabilities by incorporating cat breed detection using the YOLO (You Only Look Once) Model. Our system demonstrates an impressive average accuracy rate of 87.00% in recognizing basic emotional states in cats, effectively classifying them into predefined categories. Furthermore, we discuss the potential for deploying software applications based on this methodology on various platforms such as mobile devices and computers, making it a versatile tool for real-world applications in pet behaviour analysis and beyond.},
      keywords = {Animal-Human Interaction, Cat Breed Detection, Canny Edge Detection, Cat Emotion Recognition, Cat Facial Expression, Computer Vision, Convolutional Neural Network (CNN), Deep Learning, Emotion Classification, Facial Emotion Classification, Human-Animal Bond, Image Analysis, Machine Learning, Pet Behavior Analysis, Pet Psychology, YOLO Model.},
      month = {February},
  }