Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861-65), the first widely-photographed conflict. Many thousands of these portraits survive, but only 10--20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluation of Photo Sleuth one month after its public launch showed that it helped users successfully identify unknown portraits and provided a sustainable model for volunteer contribution. We also discuss implications for crowd-AI interaction and person identification pipelines.
more »
« less
Photo Sleuth: Combining Collective Intelligence and Computer Vision to Identify Historical Portraits
Identifying people in photographs is a critical task in a wide variety of domains, from national security
[7] to journalism [14] to human rights investigations [1]. The task is also fundamentally complex and
challenging. With the world population at 7.6 billion and growing, the candidate pool is large. Studies
of human face recognition ability show that the average person incorrectly identifies two people as
similar 20–30% of the time, and trained police detectives do not perform significantly better [11].
Computer vision-based face recognition tools have gained considerable ground and are now widely
available commercially, but comparisons to human performance show mixed results at best [2,10,16].
Automated face recognition techniques, while powerful, also have constraints that may be impractical
for many real-world contexts. For example, face recognition systems tend to suffer when the target
image or reference images have poor quality or resolution, as blemishes or discolorations may be
incorrectly recognized as false positives for facial landmarks. Additionally, most face recognition
systems ignore some salient facial features, like scars or other skin characteristics, as well as distinctive
non-facial features, like ear shape or hair or facial hair styles.
This project investigates how we can overcome these limitations to support person identification tasks.
By adjusting confidence thresholds, users of face recognition can generally expect high recall (few false
negatives) at the cost of low precision (many false positives). Therefore, we focus our work on the “last
mile” of person identification, i.e., helping a user find the correct match among a large set of similarlooking candidates suggested by face recognition. Our approach leverages the powerful capabilities of
the human vision system and collaborative sensemaking via crowdsourcing to augment the
complementary strengths of automatic face recognition. The result is a novel technology pipeline
combining collective intelligence and computer vision.
We scope this project to focus on identifying soldiers in photos from the American Civil War era (1861–
1865). An estimated 4,000,000 soldiers fought in the war, and most were photographed at least once,
due to decreasing costs, the increasing robustness of the format, and the critical events separating
friends and family [17]. Over 150 years later, the identities of most of these portraits have been lost,
but as museums and archives increasingly digitize and publish their collections online, the pool of
reference photos and information has never been more accessible. Historians, genealogists, and
collectors work tirelessly to connect names with faces, using largely manual identification methods [3,9].
Identifying people in historical photos is important for preserving material culture [9], correcting the
historical record [13], and recognizing contributions of marginalized groups [4], among other reasons.
more »
« less
- Award ID(s):
- 1651969
- NSF-PAR ID:
- 10081892
- Date Published:
- Journal Name:
- ACM Conference on Collective Intelligence (CI 2018)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
As AI-based face recognition technologies are increasingly adopted for high-stakes applications like locating suspected criminals, public concerns about the accuracy of these technologies have grown as well. These technologies often present a human expert with a shortlist of high-confidence candidate faces from which the expert must select correct match(es) while avoiding false positives, which we term the “last-mile problem.” We propose Second Opinion, a web-based software tool that employs a novel crowdsourcing workflow inspired by cognitive psychology, seed-gather-analyze, to assist experts in solving the last-mile problem. We evaluated Second Opinion with a mixed-methods lab study involving 10 experts and 300 crowd workers who collaborate to identify people in historical photos. We found that crowds can eliminate 75% of false positives from the highest-confidence candidates suggested by face recognition, and that experts were enthusiastic about using Second Opinion in their work. We also discuss broader implications for crowd–AI interaction and crowdsourced person identification.more » « less
-
null (Ed.)more » « less
Identifying people in historical photographs is important for interpreting material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861-65). Millions of these portraits survive, but only 10-20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluation of Photo Sleuth one month after its public launch showed that it helped users successfully identify unknown portraits.
-
The PoseASL dataset consists of color and depth videos collected from ASL signers at the Linguistic and Assistive Technologies Laboratory under the direction of Matt Huenerfauth, as part of a collaborative research project with researchers at the Rochester Institute of Technology, Boston University, and the University of Pennsylvania. Access: After becoming an authorized user of Databrary, please contact Matt Huenerfauth if you have difficulty accessing this volume. We have collected a new dataset consisting of color and depth videos of fluent American Sign Language signers performing sequences ASL signs and sentences. Given interest among sign-recognition and other computer-vision researchers in red-green-blue-depth (RBGD) video, we release this dataset for use by the research community. In addition to the video files, we share depth data files from a Kinect v2 sensor, as well as additional motion-tracking files produced through post-processing of this data. Organization of the Dataset: The dataset is organized into sub-folders, with codenames such as "P01" or "P16" etc. These codenames refer to specific human signers who were recorded in this dataset. Please note that there was no participant P11 nor P14; those numbers were accidentally skipped during the process of making appointments to collect video stimuli. Task: During the recording session, the participant was met by a member of our research team who was a native ASL signer. No other individuals were present during the data collection session. After signing the informed consent and video release document, participants responded to a demographic questionnaire. Next, the data-collection session consisted of English word stimuli and cartoon videos. The recording session began with showing participants stimuli consisting of slides that displayed English word and photos of items, and participants were asked to produce the sign for each (PDF included in materials subfolder). Next, participants viewed three videos of short animated cartoons, which they were asked to recount in ASL: - Canary Row, Warner Brothers Merrie Melodies 1950 (the 7-minute video divided into seven parts) - Mr. Koumal Flies Like a Bird, Studio Animovaneho Filmu 1969 - Mr. Koumal Battles his Conscience, Studio Animovaneho Filmu 1971 The word list and cartoons were selected as they are identical to the stimuli used in the collection of the Nicaraguan Sign Language video corpora - see: Senghas, A. (1995). Children’s Contribution to the Birth of Nicaraguan Sign Language. Doctoral dissertation, Department of Brain and Cognitive Sciences, MIT. Demographics: All 14 of our participants were fluent ASL signers. As screening, we asked our participants: Did you use ASL at home growing up, or did you attend a school as a very young child where you used ASL? All the participants responded affirmatively to this question. A total of 14 DHH participants were recruited on the Rochester Institute of Technology campus. Participants included 7 men and 7 women, aged 21 to 35 (median = 23.5). All of our participants reported that they began using ASL when they were 5 years old or younger, with 8 reporting ASL use since birth, and 3 others reporting ASL use since age 18 months. Filetypes: *.avi, *_dep.bin: The PoseASL dataset has been captured by using a Kinect 2.0 RGBD camera. The output of this camera system includes multiple channels which include RGB, depth, skeleton joints (25 joints for every video frame), and HD face (1,347 points). The video resolution produced in 1920 x 1080 pixels for the RGB channel and 512 x 424 pixels for the depth channels respectively. Due to limitations in the acceptable filetypes for sharing on Databrary, it was not permitted to share binary *_dep.bin files directly produced by the Kinect v2 camera system on the Databrary platform. If your research requires the original binary *_dep.bin files, then please contact Matt Huenerfauth. *_face.txt, *_HDface.txt, *_skl.txt: To make it easier for future researchers to make use of this dataset, we have also performed some post-processing of the Kinect data. To extract the skeleton coordinates of the RGB videos, we used the Openpose system, which is capable of detecting body, hand, facial, and foot keypoints of multiple people on single images in real time. The output of Openpose includes estimation of 70 keypoints for the face including eyes, eyebrows, nose, mouth and face contour. The software also estimates 21 keypoints for each of the hands (Simon et al, 2017), including 3 keypoints for each finger, as shown in Figure 2. Additionally, there are 25 keypoints estimated for the body pose (and feet) (Cao et al, 2017; Wei et al, 2016). Reporting Bugs or Errors: Please contact Matt Huenerfauth to report any bugs or errors that you identify in the corpus. We appreciate your help in improving the quality of the corpus over time by identifying any errors. Acknowledgement: This material is based upon work supported by the National Science Foundation under award 1749376: "Collaborative Research: Multimethod Investigation of Articulatory and Perceptual Constraints on Natural Language Evolution."more » « less
-
Agaian, Sos S. ; Jassim, Sabah A. (Ed.)Face recognition technologies have been in high demand in the past few decades due to the increase in human-computer interactions. It is also one of the essential components in interpreting human emotions, intentions, facial expressions for smart environments. This non-intrusive biometric authentication system relies on identifying unique facial features and pairing alike structures for identification and recognition. Application areas of facial recognition systems include homeland and border security, identification for law enforcement, access control to secure networks, authentication for online banking and video surveillance. While it is easy for humans to recognize faces under varying illumination conditions, it is still a challenging task in computer vision. Non-uniform illumination and uncontrolled operating environments can impair the performance of visual-spectrum based recognition systems. To address these difficulties, a novel Anisotropic Gradient Facial Recognition (AGFR) system that is capable of autonomous thermal infrared to visible face recognition is proposed. The main contribution of this paper includes a framework for thermal/fused-thermal-visible to visible face recognition system and a novel human-visual-system inspired thermal-visible image fusion technique. Extensive computer simulations using CARL, IRIS, AT&T, Yale and Yale-B databases demonstrate the efficiency, accuracy, and robustness of the AGFR system. Keywords: Infrared thermal to visible facial recognition, anisotropic gradient, visible-to-visible face recognition, nonuniform illumination face recognition, thermal and visible face fusion methodmore » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
