Facial Chick Sexing: An Automated Chick Sexing System From Chick Facial Image

TL;DR

This paper introduces an automated chick sexing system based on facial image analysis, aiming to replace invasive and breed-specific methods with a non-invasive, accessible approach that achieves over 81% accuracy.

Contribution

It presents a novel facial classification approach for chick sexing, reducing the need for expert knowledge and invasive procedures, and demonstrating promising accuracy.

Findings

Achieved 81.89% accuracy in chick sex classification

Developed a comprehensive system including facial detection and alignment

Validated the approach on two different facial image datasets

Abstract

Chick sexing, the process of determining the gender of day-old chicks, is a critical task in the poultry industry due to the distinct roles that each gender plays in production. While effective traditional methods achieve high accuracy, color, and wing feather sexing is exclusive to specific breeds, and vent sexing is invasive and requires trained experts. To address these challenges, we propose a novel approach inspired by facial gender classification techniques in humans: facial chick sexing. This new method does not require expert knowledge and aims to reduce training time while enhancing animal welfare by minimizing chick manipulation. We develop a comprehensive system for training and inference that includes data collection, facial and keypoint detection, facial alignment, and classification. We evaluate our model on two sets of images: Cropped Full Face and Cropped Middle Face, both of which maintain essential facial features of the chick for further analysis. Our experiment demonstrates the promising viability, with a final accuracy of 81.89%, of this approach for future practices in chick sexing by making them more universally applicable.