Person identification from arm’s hair patterns using CT-twofold Siamese network in forensic psychiatric hospitals

Abstract Identifying criminals in serious crimes from digital images is a challenging forensic task as their faces will be covered in most cases. In addition, the only available information will be hand. A single robust technique to identify the criminals from arm’s hair patterns can be a potential cost-effective and unobtrusive solution in various other areas such as in criminal psychiatric hospitals during rehabilitation to identify and track patients instead of using barcoding, radio frequency identification (RFID), and biometrics. The existing state-of-the-art methods for person identification uses convolutional neural network (CNN) and long short-term memory (LSTM)-based architectures which require the entire data to be trained once again when new data comes. To address these issues, we proposed a novel Siamese network-based architecture which not only reduces this training paradigm but also performs better than several existing methods. Since there were no standard datasets for person identification from arm’s hair patterns, we created a database with several voluntary participants by collecting their hands’ images. Several data augmentation techniques are also used to make the database more robust. The experimental results show that the proposed architecture performs better for the created database with mAP, mINP, and R1 of 94.8, 90.0, and 93.5, respectively. The proposed CTTSN performs well for the closed world person re-identification problem using soft biometric features in real time (52 frames per second).