Vein pattern visualisation and feature extraction using sparse auto-encoder for forensic purposes

Child sexual abuse is a serious global problem that has gained public attention in recent years. Due to the popularity of digital cameras, many perpetrators take images of their sexual activities. Traditionally, it has been difficult to use vein patterns in evidence images for forensic identification, because they were nearly invisible in colour images. State-of-the-art techniques, and computational methods including optical-based vein uncovering or artificial neural networks have recently been introduced to extract vein patterns for identification purposes. However, these methods are still not mature due to limitations such as lack of reliable feature extraction, efficient uncovering algorithms, and matching difficulties. In this paper, we propose two new schemes to overcome some of these limitations by using sparse auto-encoder and adaptive contrast enhancement. Specifically, an adjustment sparse auto-encoder parameters scheme is used for optimising parameters, and then optimised parameters are automatically trained to enhance the robustness of vein visualisation and feature extraction. We also use a pair of synchronised colour and near infrared NIR images to generate the skeletonised vein patterns for verifying the outcome of the proposed method. The proposed algorithm was examined on a database with 100 pairs of colour and NIR images collected from different parts of the body such as forearms, thighs, chests and ankles. The experimental results are encouraging and indicate that the proposed method improves the feature extraction procedure, which can lead to better uncovering results compared with current methods.