A fast-expanding topic is the study of palmprint biometric identification in contactless scenario, which uses techniques from computer vision and machine learning to identify and authenticate people. In this study, we utilized a handcrafted video dataset with 60 distinct classes, each labelled as either a left or right hand, to investigate palmprint detection and matching tasks. The dataset showcases various variations in palmprint patterns, like distance from the sensor, orientation, finger positioning, and deformation, making it an ideal candidate for the development of robust and accurate palmprint recognition models. The major goal of the study is to identify palmprints in the video collection and match them with the right class or pattern. To accomplish this task, different machine learning (ML) and deep learning (DL) models were trained and evaluated. To find the best method for palmprint identification in a contactless manner, the accuracy of each model was tested. In conclusion, our study adds to the expanding body of knowledge on biometric palmprint identification and introduces a fresh handmade video dataset that can be used to compare the effectiveness of various models.