Application of non-invasive scan technologies for acquisition of latent fingerprints promise a better support of forensic and dactyloscopic experts when securing evidence at crime scenes. Furthermore, non-destructive acquisition preserve the chance of subsequent chemical and forensic analysis of left residue. Based on results of an ongoing research project with sensor industry partners, this paper presents a collection of 28 statistical, gradient-, and spectral density-based features for latent fingerprint detection using low resolution scans. Within this work a chromatic white light (CWL) sensor is used for image acquisition. Furthermore, based on concepts of biometric fusion, a taxonomy for possible fusion strategies is presented and very first results for three different strategies on decision level are discussed. Experimental evaluation is performed based on scans of 1680 latent fingerprints on three different surfaces. The results show very good performance on planar, non-absorbing surfaces with uniform reflection characteristics with an detection rate of 2.51% in the best case. On the other hand difficulties are arising from surfaces with non-uniform/predictable reflection characteristics.