Online writer verification with dynamic handwriting signals is still difficult, and it has been especially under-studied for complex Southeast Asian scripts like Khmer. This work tackles online, text-independent, word-level Khmer writer verification as a pairwise decision problem: given two handwritten word samples, decide whether they were written by the same person. We introduce KVerifyID, a hybrid dual-stream Siamese network that learns from both (i) a grayscale image rendering of each word and (ii) its pen-trajectory sequence (xⓜ,yⓜ,p)with explicit pen-state encoding. The resulting modality embeddings are fused into a compact 128-dimensional writer representation, and verification is performed via cosine similarity, using thresholds selected on validation and then fixed for testing. On a Khmer online handwriting dataset collected from 298 writers (4,878 word instances) with strict writer-disjoint splits, the model generalizes strongly, achieving 99.50% training accuracy and 99.74% test accuracy, with a low verification error of 0.32% validation EER (equal error rate). At the validation equal-error operating point, the errors are FAR (false acceptance rate) = 0.30% and FRR (false rejection rate) = 0.19%. Overall, the results show that jointly leveraging spatial word appearance and online stroke dynamics enables robust Khmer writer verification, making it promising for digital authentication and forensic screening.