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Towards Recommender Systems
for Police Photo Lineup
Ladislav Peškaa and Hana Trojanováb
a Department of Software Engineeri...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
2
Recommender Systems
 Propose relev...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
3
Police Photo Lineup
 Eyewitness id...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
4
Errors in Photo Lineup
DLRS@RecSys2...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
5
Errors in Photo Lineup
DLRS@RecSys2...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
6
Assembling Fair Photo Lineups
 Sys...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
7
Assembling Fair Photo Lineups
 Fir...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
8
Research Questions
 Is candidates ...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
9
Evaluation – Protocol
 Dataset of ...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
10
Results
 In total 202 completed l...
Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
11
Results II.
 Very low intersectio...
Conclusions
 Recommending candidates based on CNN’s features seems
plausible in police photo lineup
 Item-based recommen...
DLRS@RecSys2017, Como Ladislav Peska & Hana Trojanova: Towards
Recommender Systems for Police Photo Lineup
13
Thank you!
Q...
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Towards Recommender Systems for Police Photo Lineup

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Photo lineups play a significant role in the eyewitness identifica-tion process. This method is used to provide evidence in the prosecution and subsequent conviction of suspects. Unfortu-nately, there are many cases where lineups have led to the con-viction of an innocent suspect. One of the key factors affecting the incorrect identification of a suspect is the lack of lineup fair-ness, i.e. that the suspect differs significantly from all other candidates. Although the process of assembling fair lineup is both highly important and time-consuming, only a handful of tools are available to simplify the task.
In these slides, we describe our work towards using recommend-er systems for the photo lineup assembling task. We propose and evaluate two complementary methods for item-based rec-ommendation: one based on the visual descriptors of the deep neural network, the other based on the content-based attrib-utes of persons.
The initial evaluation made by forensic technicians shows that although results favored visual descriptors over attribute-based similarity, both approaches are functional and highly diverse in terms of recommended objects. Thus, future work should in-volve incorporating both approaches in a single prediction method, preference learning based on the feedback from forensic technicians and recommendation of assembled lineups instead of single candidates.

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Towards Recommender Systems for Police Photo Lineup

  1. 1. Towards Recommender Systems for Police Photo Lineup Ladislav Peškaa and Hana Trojanováb a Department of Software Engineering, b Department of Psychology, Charles University, Prague, Czech Republic
  2. 2. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 2 Recommender Systems  Propose relevant items to the right persons at the right time  Successful on many various domains  Multimedia  E-commerce  POIs  Events  Recepies  …  However, some novel (relevant, important) tasks are yet to discover DLRS@RecSys2017, Como
  3. 3. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 3 Police Photo Lineup  Eyewitness identification of the suspect / offender during criminal proceedings  Most common case: select suspect among other persons (fillers)  Either „in natura“ or based on photographies DLRS@RecSys2017, Como - Suspect is positioned at random - 3-7 fillers are added, witness should not know them - Wittness is instructed that the offender may or may not be present
  4. 4. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 4 Errors in Photo Lineup DLRS@RecSys2017, Como 1) Observe the event 2) Identify the offender - among other candidates
  5. 5. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 5 Errors in Photo Lineup DLRS@RecSys2017, Como 1) Observe the event. - numerous sources of error (poor lighting, large distance, short time, fear, fatigue…) - affects memory and recognition processes - decrease witness certainty and reliability 2) Identify the offender - decreased certainty may lead to incorrect identification - and conviction of innocent persons
  6. 6. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 6 Assembling Fair Photo Lineups  System should be set to eliminate testimony of uncertain witnesses  Double-blind administration  Fair (unbiased) assembling of lineups  Out of the dataset of candidates, lineup administrator should select fillers similar to the suspect  Currently, only feature-based filtering systems are available  Better automatization in content processing is necessary  How to better approximate human-perceived similarity of persons?  Convolutional Neural Networks? DLRS@RecSys2017, Como
  7. 7. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 7 Assembling Fair Photo Lineups  First approach: item-based recommendations  Propose top-k candidates for each suspect  CB-RS, cosine similarity of candidates’ and suspect’s CB features  Nationality, Age, Appearence features  Baseline (mimic feature-based filters)  Visual-RS, cosine similarity of candidates’ and suspect’s VGG- Face probability layers  Approximate the similarity of a candidate and the suspect through their similarities with other persons  VGG-Face1 network  VGG network for facial recognition problem  Trained as a multi-classification problem  Dataset of 2,622 celebrities with 1,000 images each 1Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep Face Recognition. British Machine Vision Conference. DLRS@RecSys2017, Como
  8. 8. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 8 Research Questions  Is candidates recommendation (based on VGG-Face’s features) suitable for the lineup assembling?  How well is the human-perceived similarity approximated within top-k candidates?  What is the role of diversity in lineup assembling?  Is there a space for personalized recommendations (i.e. personalized per lineup administrator)?  Can there be „too similar“ candidates? DLRS@RecSys2017, Como
  9. 9. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 9 Evaluation – Protocol  Dataset of missing and wanted persons in Czech Republic  4423 males  User study, 30 different lineups, 7 forensic technicians  For each lineup, both CB-RS and Visual-RS proposed top-20 candidates  Merged into one list, forensic technician should select relevant fillers for particular suspect  Follow-up questionaire and discussion DLRS@RecSys2017, Como
  10. 10. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 10 Results  In total 202 completed lineups, 800 selected candidates  Recommendations were seemingly relevant in most cases  Visual-RS considerably outperformed CB-RS  The performance difference was much smaller for suspects from outside of central Europe  Effect of different ethnicity? (Known in the forensic psychology literature). DLRS@RecSys2017, Como Total lineups Selected candidates Level of agreement All lineups Visual-RS 202 466 (58%) 0.178 CB-RS 298 (37%) 0.138 Lineups with suspects from countries outside Central Europe Visual-RS 53 105 (51%) 0.128 CB-RS 82 (40%) 0.205
  11. 11. Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 11 Results II.  Very low intersection (1.8%) of top-20 CB-RS and Visual-RS candidates  Potential to apply some combination in the future work  Average position of selected candidate CB-RS: 8.9 (+/- 5.2); Visual-RS: 8.2 (+/- 5.7)  Ordering seems relevant even in top-20, however can be improved  Low level of agreement among participants on the selected candidates (0.178, 0.138)  Limits of the global ordering?  Potential for personalized recommendation?  Less diversity is better  Participants agreed on the need for providing homogeneous lineups  Dynamical recommendation based on the selected candidates?  No seemingly too similar candidates  However, the dataset contained some duplicates  Should be addressed in the future work DLRS@RecSys2017, Como
  12. 12. Conclusions  Recommending candidates based on CNN’s features seems plausible in police photo lineup  Item-based recommendations seems like a good first approach  There is a potential for:  Session-based personalized recommendations (need for uniformity of the final list)  Long-term preference learning (due to the level of disaggreeement among lineup administrators)  Approaches combining CNN’s features with content-based attributes Future work  Move from recommended candidates to recommended lineups  Evaluation through mock witnesses  Deployable demo DLRS@RecSys2017, Como Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 12
  13. 13. DLRS@RecSys2017, Como Ladislav Peska & Hana Trojanova: Towards Recommender Systems for Police Photo Lineup 13 Thank you! Questions, comments? Supplementary materials: http://github.com/lpeska/lineups, https://tinyurl.com/yat3rtr2 Slides: https://www.slideshare.net/LadislavPeska/towards-recommender-systems-for-police-photo-lineup

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