In the field of person re-identification (re-ID), accurately matching individuals across different camera views poses significant challenges due to variations in pose, illumination, viewpoint, and notably, scale. Traditional methods in re-ID have focused on robust feature descriptor generation and sophisticated metric learning, yet they often fall short in addressing scale variations effectively. In this work, we introduce a novel approach to scale-invariant person re-ID through the development of our scale-invariant residual networks coupled with an innovative batch adaptive triplet loss function for enhanced deep metric learning. The first network, termed Scale-Invariant Triplet Network (SI-TriNet), leverages pre-trained weights to form a deeper architecture, while the second, Scale-Invariant Siamese Resnet-32 (SISR-32), is a shallower structure trained from scratch. These networks are adept at handling scale variations, a common yet challenging aspect in re-ID tasks, by employing scale-invariant (SI) convolution techniques that ensure robust feature detection across multiple scales. This is complemented by our proposed batch adaptive triplet loss function that refines the metric learning process, dynamically prioritizing learning from harder positive samples to improve the model’s discriminatory capacity. Extensive evaluation on benchmark datasets Market-1501 and CUHK03 demonstrates the superiority of our proposed methods over existing state-of-the-art approaches. Notably, SI-TriNet and SISR-32 show significant improvements in both mean Average Precision (mAP) and rank-1 accuracy metrics, affirming the efficacy of our scale-invariant architectures and the novel loss function in addressing the complexities of person re-ID. This study not only advances the understanding of scale-invariant feature learning in deep networks but also sets a new benchmark in the person re-ID domain, promising more accurate and scalable solutions for real-world surveillance and security applications.