Latent fingerprints are unintentional finger skin impressions left as invisible ridge patterns
at crime scenes or objects. A major challenge in latent fingerprint forensics is the poor quality of the
lifted image from the crime scene captured by investigators. Forensics investigators are in permanent
search of novel breakthroughs in effective technologies to capture and process such low-quality
images. The accuracy of the recognition often depends upon 1) the quality of the image captured at
the beginning, 2) metrics used to assess the quality and thereafter 3) the level of enhancement
required. Low-performance scanners, unstructured background noise, poor ridge quality,
overlapping structured noise, etc. are often reasoned for poor image quality. Insufficient image
quality results in the detection of false minutiae and hence reduces the recognition rate.
Traditionally, image segmentation and enhancement are manually carried out from highly skilled
experts. The use of an automated system is definitely challenging and can only be effective, if a
significant amount of time is saved. This survey amplifies a comparative study of various
segmentation techniques available for latent fingerprint forensics.
Keywords: Latent fingerprint, image segmentation, feature selection, forensics science, crime scenes, finger skin impressions.
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