The Proficiency of Experts

Expert evidence plays a crucial role in civil and criminal litigation. Changes in the rules concerning expert admissibility, following the Supreme Court\u27s Daubert ruling, strengthened judicial review of the reliability and the validity of an expert\u27s methods. Judges and scholars, however, have neglected the threshold question for expert evidence: whether a person should be qualified as an expert in the first place. Judges traditionally focus on credentials or experience when qualifying experts without regard to whether those criteria are good proxies for true expertise. We argue that credentials and experience are often poor proxies for proficiency. Qualification of an expert presumes that the witness can perform in a particular domain with a proficiency that non-experts cannot achieve, yet many experts cannot provide empirical evidence that they do in fact perform at high levels of proficiency. To demonstrate the importance ofproficiency data, we collect and analyze two decades of proficiency testing of latent fingerprint examiners. In this important domain, we found surprisingly high rates of false positive identifications for the period 1995 to 2016. These data would qualify the claims of many fingerprint examiners regarding their near infallibility, but unfortunately, judges do not seek out such information. We survey the federal and state case law and show how judges typically accept expert credentials as a proxy for proficiency in lieu of direct proof of proficiency. Indeed, judges often reject parties\u27 attempts to obtain and introduce at trial empirical data on an expert\u27s actual proficiency. We argue that any expert who purports to give falsifiable opinions can be subjected to proficiency testing and that proficiency testing is the only objective means of assessing the accuracy and reliability ofexperts who rely on subjective judgments to formulate their opinions (so-called black-box experts ). Judges should use proficiency data to make expert qualification decisions when the data is available, should demand proof of proficiency before qualifying black-box experts, and should admit at trial proficiency data for any qualified expert. We seek to revitalize the standard for qualifying experts: expertise should equal proficiency

Enhanced convnet based Latent Finger Print Recognition

Latent finger print recognition plays an important role in forensic, criminal cases etc. The latent images will not be recognised easily since they are impartial images, which find difficult to match with the registered database. Due to noisy images, it is very difficult for recognition. Autoencoder plays an important role in pre-processing the latent image. ConvNetbased method is an efficient approach used for latent image recognition. For each minutiae extraction, ConvNet descriptor is performed. Both minutiae and texture matcher is considered for comparison. This technique is compared with existing methods which shows, that the proposed method provides a higher accuracy than the existing methods like CNN, skeleton approach nonlinear mapping and product quantization. The proposed method provides an accuracy of 76.4%, 80.4% and 86.4% for rank1,5 and 10 respectively

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research