Training methods for facial image comparison: a literature review

This literature review was commissioned to explore the psychological literature relating to facial image comparison with a particular emphasis on whether individuals can be trained to improve performance on this task. Surprisingly few studies have addressed this question directly. As a consequence, this review has been extended to cover training of face recognition and training of different kinds of perceptual comparisons where we are of the opinion that the methodologies or findings of such studies are informative. The majority of studies of face processing have examined face recognition, which relies heavily on memory. This may be memory for a face that was learned recently (e.g. minutes or hours previously) or for a face learned longer ago, perhaps after many exposures (e.g. friends, family members, celebrities). Successful face recognition, irrespective of the type of face, relies on the ability to retrieve the to-berecognised face from long-term memory. This memory is then compared to the physically present image to reach a recognition decision. In contrast, in face matching task two physical representations of a face (live, photographs, movies) are compared and so long-term memory is not involved. Because the comparison is between two present stimuli rather than between a present stimulus and a memory, one might expect that face matching, even if not an easy task, would be easier to do and easier to learn than face recognition. In support of this, there is evidence that judgment tasks where a presented stimulus must be judged by a remembered standard are generally more cognitively demanding than judgments that require comparing two presented stimuli Davies & Parasuraman, 1982; Parasuraman & Davies, 1977; Warm and Dember, 1998). Is there enough overlap between face recognition and matching that it is useful to look at the literature recognition? No study has directly compared face recognition and face matching, so we turn to research in which people decided whether two non-face stimuli were the same or different. In these studies, accuracy of comparison is not always better when the comparator is present than when it is remembered. Further, all perceptual factors that were found to affect comparisons of simultaneously presented objects also affected comparisons of successively presented objects in qualitatively the same way. Those studies involved judgments about colour (Newhall, Burnham & Clark, 1957; Romero, Hita & Del Barco, 1986), and shape (Larsen, McIlhagga & Bundesen, 1999; Lawson, Bülthoff & Dumbell, 2003; Quinlan, 1995). Although one must be cautious in generalising from studies of object processing to studies of face processing (see, e.g., section comparing face processing to object processing), from these kinds of studies there is no evidence to suggest that there are qualitative differences in the perceptual aspects of how recognition and matching are done. As a result, this review will include studies of face recognition skill as well as face matching skill. The distinction between face recognition involving memory and face matching not involving memory is clouded in many recognition studies which require observers to decide which of many presented faces matches a remembered face (e.g., eyewitness studies). And of course there are other forensic face-matching tasks that will require comparison to both presented and remembered comparators (e.g., deciding whether any person in a video showing a crowd is the target person). For this reason, too, we choose to include studies of face recognition as well as face matching in our revie

What Makes Natural Scene Memorable?

Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable. However, a clear understanding and reliable estimation of natural scene memorability remain elusive. In this paper, we provide an attempt to answer: "what exactly makes natural scene memorable". Specifically, we first build LNSIM, a large-scale natural scene image memorability database (containing 2,632 images and memorability annotations). Then, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of natural scene. In particular, we find that high-level feature of scene category is rather correlated with natural scene memorability. Thus, we propose a deep neural network based natural scene memorability (DeepNSM) predictor, which takes advantage of scene category. Finally, the experimental results validate the effectiveness of DeepNSM.Comment: Accepted to ACM MM Workshop

Focal Spot, Spring 1989

https://digitalcommons.wustl.edu/focal_spot_archives/1051/thumbnail.jp

Determinants of estimated face composite quality

Includes abstract.Includes bibliographical references (p. 142-157 ).This thesis addresses the evaluation of an investigative tool commonly used by police forces around the world, namely a face composite or facial likeness. The process of constructing a composite involves a number of cognitive elements, all of which contribute to the final composite quality. This thesis examines elements of the construction process and assessment of the final composite quality in research and practice. There are three main aspects to the empirical work reported here. The first, consisting of two experimental studies, investigates the possibility of reinstating context as a way of improving composite quality. The second examines composite construction and use within the South African Police Service. The third examined the measurement of composite quality itself

Modeling Information Diffusion in University Libraries: Assessing Peer Interaction Patterns as a Complex Dynamic System

This presentation was offered as part of the CUNY Library Assessment Conference, Reinventing Libraries: Reinventing Assessment, held at the City University of New York in June 2014

A Framework for Harmonizing Forensic Science Practices and Digital/Multimedia Evidence

Like many other specializations within forensic science, the digital/multimedia discipline has been challenged with respect to demonstrating that the processes, activities, and techniques used are sufficiently scientific. To address this issue, in April 2015, the Organization of Scientific Area Committees for Forensic Science (OSAC) Digital/Multimedia Scientific Area Committee (SAC) established a Task Group (TG). This document summarizes the work of the TG that grew into establishing a harmonizing framework for forensic science practices and digital/multimedia evidence. The TG researched and deliberated on the essential elements of digital/multimedia science, the nature of evidence examined, the overarching scientific principles and reasoning processes, the questions addressed by core forensic processes, and the activities and techniques which support the core forensic processes. It reviewed a large volume of pertinent literature, conducted interviews of practitioners, academics, and other interested parties. Over a three-year period and many hours of debate, more than 40 discussion drafts were produced. The TG determined that digital/multimedia evidence, and other forensic disciplines, would be in a much stronger position to demonstrate their scientific basis as a harmonized forensic science rather than as mere disciplines at the intersection of forensic specialties and other sciences. The value of forensic science as a whole is that it uses scientific reasoning and processes within the framework articulated in this document to address questions – specific to an event or a case – for legal contexts, to provide decision-makers with trustworthy understanding of the traces in order to help them make decisions. The TG considered how the definitions and framework developed in the context of digital/multimedia evidence mesh with forensic science as a whole. The present document describes the concept of traces as the core nature of forensic evidence and the fundamental object of study in forensic science. It proposes a broad definition of forensic science, not limited to legal problems in civil and criminal justice systems (courtroom contexts), and describes the different types of reasoning that play a significant role in forensic science. Then it defines five core forensic processes, seven forensic activities, and three operational techniques. The formalization of forensic science reasoning processes and outcomes in this work leads to increased reliability, repeatability, and validation in forensic results. This, in turn, gives decision-makers increased confidence in and understanding of forensic results. The resulting definitions and framework can be used to harmonize concepts and practices within digital/multimedia science, and are likely applicable to most forensic disciplines. As such, this work may be useful in articulating their scientific basis, and promoting forensic science as one science, which is more than the union of a patchwork of forensic disciplines. The new paradigm created by the digital realm brings a unique opportunity to revisit fundamental definitions in forensic science and to strengthen the identity of forensic science as a whole, unified by common principles and processes that can address questions for legal contexts. This document represents the conclusions and recommendations of the TG as of the date of its writing. The work continues and future versions of this document can be expected to contain new observations and updated conclusions

AI-Generated Fashion Designs: Who or What Owns the Goods?

As artificial intelligence (“AI”) becomes an increasingly prevalent tool in a plethora of industries in today’s society, analyzing the potential legal implications attached to AI-generated works is becoming more popular. One of the industries impacted by AI is fashion. AI tools and devices are currently being used in the fashion industry to create fashion models, fabric designs, and clothing. An AI device’s ability to generate fashion designs raises the question of who will own the copyrights of the fashion designs. Will it be the fashion designer who hires or contracts with the AI device programmer? Will it be the programmer? Or will it be the AI device itself? Designers invest a lot of talent, time, and finances into designing and creating each article of clothing and accessory it releases to the public; yet, under the current copyright standards, designers will not likely be considered the authors of their creations. Ultimately, this Note makes policy proposals for future copyright legislation within the United States, particularly recommending that AI-generated and AI-assisted designs be copyrightable and owned by the designers who purchase the AI device

Full Issue 10.3

Hannibal and St. Joseph Railroad Co.The original of this document is in the Stevens Family Papers, #1210, at the Division of Rare and Manuscript Collections, Cornell University Library, Ithaca, New York 14853