Animal Classification Using Facial Image

The increasing need for wildlife monitoring and animal safety has led to the demand for intelligent and automated systems capable of identifying animal species efficiently. Manual animal identification is time-consuming, prone to human error, and ineffective in large-scale applications. This research proposes a deep learning–based Animal Classification System using facial images to detect and recognize animal species in real-time. The system employs advanced convolutional neural networks (CNN) and YOLO (You Only Look Once) for feature extraction and classification. The proposed model demonstrates high detection accuracy, scalability, and low latency suitable for integration in highways, wildlife reserves, and farm monitoring systems. The outcomes indicate that the system significantly reduces false positives while ensuring precise species recognition even under varied environmental conditions.