In order to reduce the search time and computational complexity of an identification system, the classification procedure is a vital task. In this regard, gender classification and age estimation aim to reduce the number of comparisons, especially on large-scale databases for biometric recognition purposes. During the last decade, palm vein recognition systems are highlighted for security reasons, however, their applications for massive identification are limited. On the other hand, extreme learning machines (ELMs) have been used by the machine learning community owing to their robust performance as well as fast training times. In this paper, we introduce original and regularized ELMs to classify individuals' age and gender by using only palm vein images. In terms of overall performance and computational cost, our methods outperform the reported approach in the literature, which is based on the local binary patterns and k-nearest neighbors classifier on the VERA palm vein dataset.