Introduction

1. Models for facial recognition and body weight to more precisely provide individual pig care J.J. McGlone*1, B.L. Backus1, K. Guay1, J. Ao2, Q. Wan2, B. Nutter2, R. Pal2, S. Mitra21 Laboratory of Animal Behavior, Physiology and Welfare 2 Department of Electrical and Computer EngineeringTexas Tech University, Lubbock, TX 79409-2141 a) • Results • Cross-validation reached a 72% level of accuracy for direct hits. • The mean distance in pixels between eyes for each pig was used to relate to body weight (pig faces grow in proportion to body weight). • Over a range of body weights from birth to 100 kg, an exponential curve (y=71.97*x1/3) described the relationship between the weight of pigs and the distance between the eyes in pixels (r2= 0.9874). • For the narrow weight range of 5 to 42 kg, a linear model (y = 2.915*x +129.5) described the relationship between distance between the eyes in pixels and body weight (r2 = 0.9925). • Introduction • 110 Million pigs are marketed each year in the United States with the average worker only spending one hour per day with 1,000 pigs, or 3.6 seconds per pig. • An automated solution able to transmit individual pig information to farm management would provide welfare and management at the individual pig level. • The ideal system would recognize individual pigs and then collect data on water intake, body weight, and surface and core temperature, then mark the pigs that reach criteria for stockperson identification and action. This system would identify sick pigs that need treatment and care, and recognize pigs ready for market within a tight, valuable market weight. • Objectives • To develop software for individual pig recognition • To estimate the body weight of pigs using facial dimensions b) Methods After weaning, 21 d old, pigs were housed with littermates in 4 pens of 10 pigs (N=40). Weekly body weights and pictures were taken of each pig’s face. Specific facial features were captured by image analysis. Several analysis systems were tested, and the Eigenface algorithm was used to estimate individual pig identity. Each face image in a training set represents a linear combination of the principal components of the distribution of faces. These are called eigenvectors and displayed as Eigenfaces, which characterize the variation in faces from statistical computation of the covariance matrix of a set of face images involved. The Eigenface sums the pixels in an image to generate a weighting vector that, with some variation, is unique for that individual. The Eigenface weight vector was calculated for each pig face image and tested to determine if it could reliably indentify individuals. If the test image was correctly recognized as the most matched, we called it a ‘direct hit.’ Figure 1. Pictures of individual pig faces (n=40) were taken weekly in the same position. The camera was a set distance away from the nose of the pig so number of pixels between the eyes could be calculated. Figure 3. Eigenfaces generated in the facial recognition. ‘Test’ Eigenfaces in the blue rectangle were used for face recognition from training images 1-19. Figure 4. The (a) exponential and (b) linear relationship between pig weight (in lbs on Y axis) and distance between the eyes in pixels on the X axis. The dotted line denotes 5% deviation from the estimated weights. Conclusions Labor represents only 3.2% of the cost to produce a market pig from weaning. With such a low allocation to labor, the individual pig receives very little attention by human caretakers. These early findings will be key components in an automated monitoring system that will have significant impact on farm management and welfare at the individual pig level. TH176 Figure 2. The step by step procedure of the Eigenface-based face recognition method. Broken arrows indicate the validation steps.