An enhanced feature set for pattern recognition based contrast enhancement of contact-less captured latent fingerprints in digitized crime scene forensics

Abstract

In crime scene forensics latent fingerprints are found on various substrates. Nowadays primarily physical or chemical preprocessing techniques are applied for enhancing the visibility of the fingerprint trace. In order to avoid altering the trace it has been shown that contact-less sensors offer a non-destructive acquisition approach. Here, the exploitation of fingerprint or substrate properties and the utilization of signal processing techniques are an essential requirement to enhance the fingerprint visibility. However, especially the optimal sensory is often substrate-dependent. An enhanced generic pattern recognition based contrast enhancement approach for scans of a chromatic white light sensor is introduced in Hildebrandt et al.1 using statistical, structural and Benford's law2 features for blocks of 50 micron. This approach achieves very good results for latent fingerprints on cooperative, non-textured, smooth substrates. However, on textured and structured substrates the error rates are very high and the approach thus unsuitable for forensic use cases. We propose the extension of the feature set with semantic features derived from known Gabor filter based exemplar fingerprint enhancement techniques by suggesting an Epsilon-neighborhood of each block in order to achieve an improved accuracy (called fingerprint ridge orientation semantics). Furthermore, we use rotation invariant Hu moments as an extension of the structural features and two additional preprocessing methods (separate X- and Y Sobel operators). This results in a 408-dimensional feature space. In our experiments we investigate and report the recognition accuracy for eight substrates, each with ten latent fingerprints: white furniture surface, veneered plywood, brushed stainless steel, aluminum foil, Golden-Oak veneer, non-metallic matte car body finish, metallic car body finish and blued metal. In comparison to Hildebrandt et al.,1 our evaluation shows a significant reduction of the error rates by 15.8 percent points on brushed stainless steel using the same classifier. This also allows for a successful biometric matching of 3 of the 8 latent fingerprint samples with the corresponding exemplar fingerprint on this particular substrate. For contrast enhancement analysis of classification results we suggest to use known Visual Quality Indexes (VQI)3 as a contrast enhancement quality indicator and discuss our first preliminary results using the exemplary chosen VQI Edge Similarity Score (ESS),4 showing a tendency that higher image differences between a substrate containing a fingerprint and a substrate with a blank surface correlate with a higher recognition accuracy between a latent fingerprint and an exemplar fingerprint. Those first preliminary results support further research into VQIs as contrast enhancement quality indicator for a given feature space.