Poster by Ladislav Peska and Hana Trojanova from the SISAP 2018 conference, Lima, Peru

1. Towards Similarity Models in Police Photo
Lineup Assembling TasksLadislav Peška
Department of Software Engineering
Charles University in Prague, Czech Republic
ABSTRACT
Police photo lineups play a significant role
in the eyewitness identification process.
Unfortunately, there are many cases where
lineups have led to the incorrect
identification and conviction of innocent
suspects. One of the key factors affecting
the incorrect identification is the lack of
lineup fairness, i.e. that the suspect differs
significantly from other candidates.
Our current task to define a suitable inter-
person similarity metric and use it in a
lineup candidates recommender system.
We evaluated a combination of DCNN
descriptors of candidates’ photos and their
content-based features, which is further
tuned by the feedback of domain experts.
LinRec recommending algorithm further
considers the need for uniformity in
lineups. LinRec method was evaluated in a
realistic user study focused on lineup
fairness over solutions proposed by
domain experts.
IDENTIFICATION PROCESS and ERRORS IN PHOTO LINEUPS
RESULTS
• Precision (fraction of false identifications; the higher the better)
• Effective lineup size (ES, considering individual contribution of all
fillers; the higher the better)
• High variance in per-lineup results (easy vs. difficult suspects)
• No signifficance between mock witnesses’ age, education, gender,
or time to decision and correctness of identification
• Dataset size and variety is crucial (common problem in practice)
ONLINE EVALUATION: Mock witnesses + lineup fairness
• Dataset of over 4400 males, portrait photos, mostly Czech,
Vietnamese, Ukrainian and Slovak nationality
• Eight simulated suspects (4 Czech, 4 Vietnamese)
• Lineups assembled by the proposed method and 3 domain
experts, 6 persons in each lineup
• Short textual description of all 8 suspects
• Voluneers were asked to read a description of the suspect and
then try to identify him in the presented lineup
Should the lineup be completely fair, the success of mock witnesses
should be similar to a random guess
• In total 108 participants, mostly Czech
• Correct identification in 56% of cases
• Similar performance of proposed method with domain
experts => considerable reduction of manual labor
FUTURE WORK
- Co-operative dynamic lineups creation (recommendations + domain expert), demo application
- Local visual similarity (i.e. show only candidates with certain visual feature)
- Augumented rare features (i.e., hair style, tattoo) or substantial dataset extensions (legal complications)
- Explore boundaries of lineup fairness (e.g., are there too similar pairs of candidate-suspect?)
LinRec METHOD: Siamese network + enhanced uniformity
Hana Trojanová
Department of Psychology
Charles University in Prague, Czech Republic
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
• conviction of innocent persons
• Witnesses may follow the „best available choice“
selection
Real offender
might have not
been present in
the lineup
𝐿𝑂𝑆𝑆𝑆𝑖𝑎𝑚 =
𝑜 𝑖,𝑜 𝑗,𝑦 𝑖,𝑗)∈𝑇𝑠
IF 𝑦𝑖,𝑗 = 1: 𝑓𝑖 − 𝑓𝑗 2
2
ELSE: ma x( 𝐶 − 𝑓𝑖 − 𝑓𝑗 2
, 0
2
While assembling lineups, consider similarity to suspect as
well as similarities to already selected lineup fillers
ES = 𝐑 − 𝐑𝐑0
𝑐 𝑖∈𝐑
ma x( 0,
1
𝐑𝐑0
− 𝑓𝑖)
Lineup LinRec Expert 1 Expert 2 Expert 3
1 0.34 / 2.70 0.60 / 3.90 0.36 / 2.45 0.11 / 1.32
2 0.68 / 3.91 0.75 / 4.19 0.33 / 2.67 0.50 / 3.50
3 0.44 / 2.58 0.26 / 2.05 0.55 / 1.91 0.75 / 3.50
4 0.74 / 3.74 0.19 / 1.76 0.72 / 4.11 0.59 / 2.91
5 0.05 / 1.16 0.13 / 1.38 0.24 / 1.71 0.39 / 2.56
6 0.30 / 2.48 0.81 / 4.13 0.33 / 2.33 0.56 / 3.81
7 0.64 / 3.90 0.53 / 3.76 0.76 / 4.43 0.80 / 3.75
8 0.25 / 2.27 0.23 / 1.69 0.40 / 2.20 0.12 / 1.48
AVG(ES) 2.84 +/- 0.96 2.86 +/- 1.24 2.73 +/- 1.00 2.85 +/- 0.99
Per-lineup results: Precision / ES. Best results are bold, second best are underlined
Demo app: herkules.ms.mff.cuni.cz/lineit
Evaluation: herkules.ms.mff.cuni.cz/lineit-eval
Source codes & results: github.com/lpeska/LineRec
Contact: peska@ksi.mff.cuni.cz
This work was supported by grants GAUK-232217 and GACR-17-22224S