DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. The analysis of DNA gel fingerprint images usually consists of five consecutive steps: image pre-processing, lane segmentation, band detection, normalization and fingerprint comparison. In this article, we firstly survey the main methods that have been applied in the literature in each of these stages. Secondly, we focus on lane-segmentation and band-detection algorithms-as they are the steps that usually require user-intervention-and detect the seven core algorithms used for both tasks. Subsequently, we present a benchmark that includes a data set of images, the gold standards associated with those images and the tools to measure the performance of lane-segmentation and band-detection algorithms. Finally, we implement the core algorithms used both for lane segmentation and band detection, and evaluate their performance using our benchmark. As a conclusion of that study, we obtain that the average profile algorithm is the best starting point for lane segmentation and band detection.