Facial Expression Analysis for Efficient Disease Classification in Sheep Using a 3NM-CTA and LIFA-Based Framework
Trapti Jain, Tanvir Habib Sardar, Trapti Jain, M. S. Guru Prasad, P. Naresh
Abstract
Facial Expression (FE) in sheep is analysed to distinguish normal and abnormal sheep behaviours that are crucial for health monitoring and welfare assessment. However, the prevailing techniques failed to concentrate on the sheep disease type, which hindered the practical treatment analysis. Therefore, the proposed work used 3 Non-Monotonic Mish Convoluted T-Max Average Neural Network (3NM-CTA) and Lyapunov Integrated Fuzzy Algorithm (LIFA) to efficiently detect the sheep disease type for a faster and more accurate treatment process. First, the sheep images are pre-processed, and the sheep's facial parts are detected using the Ortho-Greedy Viola Jonas Initialization Algorithm (OGVJI). Then, the facial landmarks are detected for each part, and the features of the landmarked eye, ear, nose, and mouth are extracted. Meanwhile, from the eye landmarks, the eye aspect ratio is calculated to predict Blepharospasm in sheep. After that, 3NM-CTA predicts the normal and abnormal sheep. Here, for abnormal sheep, the PH value is estimated. Finally, the estimated PH value and calculated aspect ratio are given to LIFA, which categorizes Acidosis, Mastitis, Listeriosis, and Blepharospasm in sheep using a minimum Fuzzification Time of 3465 ms, respectively. Thus, the proposed work outperformed the traditional methodologies.