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MATEC Web of Conferences 336, 06008 (2021) https://doi.org/10.1051/matecconf/202133606008
CSCNS2020
Forensic face recognition based on KDE and
evidence theory
Wen Xiao1, *
1JiangXi Police College, NanChang, JiangXi, China
Abstract. Forensic face recognition (FFR) has been studied in recent years
in forensic science. Given an automatic face recognition system, output
scores of the system are used to describe the similarity of face image pairs,
but not suitable for forensics. In this study, a score-mapping model based on
kernel density estimation (KDE) and evidence theory is proposed. First,
KDE was used to generate probability density function (PDF) for each
dimensional feature vector of face image pairs. Then, the PDFs could be
utilized to determine separately the basic probability assignment (BPA) of
supporting the prosecution hypothesis and the defence hypothesis. Finally,
the BPAs of each feature were combined by Dempster’s rule to get the final
BPA, which reflects the strength of evidence support. The experimental
results demonstrate that compared with the classic KDE-based likelihood
ratio method, the proposed method has a better performance in terms of
accuracy, sensitivity and specificity.
1 Introduction
In the context of forensic science, face recognition approaches have fallen into two main
categories: Subjective-based and objective-based methods. The subjective-based methods are
the traditional forensic means in the past few decades, and face biometric features have been
commonly used to inspect and compare static images for such methods [1,2]. Mainly four
subjective-based methods can be used during the analysis and comparison phase [1]: Holistic
Comparison, Morphological Analysis, Photo-anthropometry, and Superimposition. Facial
Identification Scientific Working Group (FISWG) recommends morphological analysis by
trained examiners as the primary method of comparison [3]. Moreover, in recent years, soft
Biometrics, such as gender, age, race, skin colour, spots and other characteristics, have been
considered into face recognition procedure so as to improve recognition results [4,5]. But
these methods need to be manually carried out by forensic experts, so they heavily depend
on the experience and knowledge of the forensic experts. On the other hand, the objective-
based methods attempt to identify faces using automatic face recognition [6-10]. Using
automatic recognition system to verify faces can not only improve the efficiency of forensic
work, but also promote the standardization of the forensic process. In commercial face
recognition systems, the similarity or distance between two faces is usually reported in terms
of one or several score values, which is so called “Score-based procedures” [11]. In order to
*
Corresponding author: shauven@126.com
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons
Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
MATEC Web of Conferences 336, 06008 (2021) https://doi.org/10.1051/matecconf/202133606008
CSCNS2020
take the differences and typicality of the population into account and allow for comparisons
between facial scores from different face recognition systems, there is necessary to convert
the score to Likelihood Ratio (LR) value [12]. Some organizations such as the European
Network of Forensic Science Institutes (ENFSI), report the certainty of the statement
match/nonmatch via a quantifiable amount, that is, verify whether it is the same
person/different person or not [13]. To that end, ENFSI enforces the use of a LR value to
evaluate the strength of evidence as the degree of supporting the appraisal conclusion, namely,
it can be regarded as the mensurable method to express the confidence in the match/nonmatch
decision [10]. A suitable approach to achieve this is to append such a score-to-LR mapping
in a post-processing step to an existing score-producing facial recognition system [14]. Once
a model for score-to-LR mapping has been set up, the strength of evidences can be obtained
by plugging the scores into the model.
2 Evidence evaluation and evidence theory
Evidence evaluation has been proposed in recent years as a logical and appropriate way to
report evidence to a court of law using a Bayesian probabilistic framework. LR is based on
Bayes’ rule, it is defined as the ratio of the probabilities of two hypotheses [15]: the null
hypothesis of the prosecution (Hp), and the alternative hypothesis of the defense (Hd). The
hypothesis of the prosecution Hp means that the evidences are from the same source, and the
hypothesis of the defense Hd means that the evidences are from the different source. Then
LR is obtained from two conditional probabilities, that is, the conditional probability of the
prosecution hypothesis divided by the conditional probability of the defense hypothesis. So,
the LR is defined as follow:
Pr�𝐸𝐸𝐸𝐸|𝐻𝐻𝐻𝐻𝑝𝑝𝑝𝑝 �
𝐿𝐿𝐿𝐿�𝐻𝐻𝐻𝐻𝑝𝑝𝑝𝑝 , 𝐻𝐻𝐻𝐻𝑑𝑑𝑑𝑑 , 𝐸𝐸𝐸𝐸� = (1)
Pr(𝐸𝐸𝐸𝐸|𝐻𝐻𝐻𝐻𝑑𝑑𝑑𝑑 )
In order to express the strength of evidence support, especially for convenient to
communicate evidence values in the courtroom, it would be useful to translate the numerical
expression to a verbal counterpart. One of the current frameworks to relate verbal and
numerical likelihood ratios is defined as follows [16]:
Table 1. Relation of verbal and numerical LR.
LR range Evidence to support Hp
1-2 no assistance
2-10 slightly more probable
10-100 more probable
100-10,000 much more probable
10,000 -1,000,000 far more probable
>1,000,0000 exceedingly more probable
2.1 Score-to-LR conversion model
When an automated system is used to calculate the similarity or the distance between the two
faces to be compared, it returns a score. This score itself has no forensic relevance and needs
to be converted to LR.
Four score-to-LR conversion models have been proposed [17]: Kernel Density Estimation
(KDE), Linear Logistic Regression (LLR), Histogram Binning and Pool Adjacent Violators
(PAV), where KDE is a commonly used method which is easy to explain. In KDE, a kernel
distribution is a non-parametric representation of the probability density function (PDF) of a
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random variable. It is used when a parameter distribution cannot properly describe the data,
or to avoid making assumptions about the data distribution. A kernel distribution is defined
by a smoothing function and a bandwidth value h, which controls the smoothness of the
resulting density curve. In other words, it is a technique that lets you create a smooth curve
given a set of data. It is given by the following equation:
𝑛𝑛𝑛𝑛 𝑛𝑛𝑛𝑛
1 1 𝑥𝑥𝑥𝑥 − 𝑥𝑥𝑥𝑥𝑖𝑖𝑖𝑖
𝑓𝑓𝑓𝑓𝑘𝑘𝑘𝑘 (𝑥𝑥𝑥𝑥; ℎ, 𝐾𝐾𝐾𝐾) = � 𝐾𝐾𝐾𝐾ℎ (𝑥𝑥𝑥𝑥 − 𝑥𝑥𝑥𝑥𝑖𝑖𝑖𝑖 ) = � 𝐾𝐾𝐾𝐾 � � (2)
𝑛𝑛𝑛𝑛 𝑛𝑛𝑛𝑛ℎ ℎ
𝑖𝑖𝑖𝑖=1 𝑖𝑖𝑖𝑖=1
where K is the kernel and h is the bandwidth. The kernel smoothing function K defines the
shape of the curve used to generate the probability distribution function, and the bandwidth
h steers the smoothness of the resulting approximation. Gaussian distribution is usually used
as kernel function, and the bandwidth could be adaptively generated from the sample data.
Unlike a histogram, which places the values into discrete bins, a kernel distribution sums the
component smoothing functions for each data value to produce a smooth, continuous
probability curve.
Use the kernel function to estimate the data from the same source (the prosecution
hypothesis Hp) and the data from different sources (the prosecution hypothesis Hd)
respectively, LR is calculated with the following:
Pr�𝑠𝑠𝑠𝑠�𝐻𝐻𝐻𝐻𝑝𝑝𝑝𝑝 � 𝑓𝑓𝑓𝑓𝑝𝑝𝑝𝑝 (𝑠𝑠𝑠𝑠; ℎ, 𝑘𝑘𝑘𝑘)
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿(𝑠𝑠𝑠𝑠) = = (3)
Pr(𝑠𝑠𝑠𝑠|𝐻𝐻𝐻𝐻𝑑𝑑𝑑𝑑 ) 𝑓𝑓𝑓𝑓𝑑𝑑𝑑𝑑 (𝑠𝑠𝑠𝑠; ℎ, 𝑘𝑘𝑘𝑘)
2.2 Evidence theory
Evidence theory is the generalization of probability theory [18,19], which can handle
uncertainty, impreciseness, and unknown information and fuse multi-source information
without depending on prior information.
A set of finite mutually exclusive hypotheses or propositions Ω is called the frame of
discernment, and the Basic Probability Assignment (BPA) function under the frame of
discernment is a function 𝑚𝑚𝑚𝑚: 2Ω ↦ [0,1], and satisfies with ∑𝐴𝐴𝐴𝐴⊆Ω 𝑚𝑚𝑚𝑚(𝐴𝐴𝐴𝐴) = 1 and 𝑚𝑚𝑚𝑚(Φ) = 0.
The BPA m is also called the mass function, and 𝑚𝑚𝑚𝑚(𝐴𝐴𝐴𝐴) expresses the proportion of all
relevant and available evidence that supports the claim that a particular element of 2Ω belongs
to the set A but to no particular subset of A. Suppose H1 and H2 are two independent evidence
with two mass functions m1 and m2 in the same frame of discernment Ω; the Dempster’s rule
of combination is defined as follow:
𝑚𝑚𝑚𝑚1 ⨁ 2 (𝐴𝐴𝐴𝐴) = 𝐾𝐾𝐾𝐾 −1 ∙ � 𝑚𝑚𝑚𝑚1 (𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 )𝑚𝑚𝑚𝑚2 �𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗 � (4)
𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 ∩𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗 =𝐴𝐴𝐴𝐴
where, 𝐾𝐾𝐾𝐾 = ∑𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 ∩𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗 ≠Φ 𝑚𝑚𝑚𝑚1 (𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 )𝑚𝑚𝑚𝑚2 �𝐶𝐶𝐶𝐶𝑗𝑗𝑗𝑗 � is referred to as the degree of conflict between the two
BPAs. If K is close to 0, the Dempster’s rule of combination becomes invalid. One of the
methods to solve this problem is to set discount coefficient, which usually represents the
unreliability or dependence of the evidences. If m is a BPA and (1-α) is its corresponding
discount coefficient, then the BPA after discounting is:
𝛼𝛼𝛼𝛼 ⋅ 𝑚𝑚𝑚𝑚(𝐴𝐴𝐴𝐴) 𝐴𝐴𝐴𝐴 ≠ Ω
𝑚𝑚𝑚𝑚𝛼𝛼𝛼𝛼 (𝐴𝐴𝐴𝐴) = � (5)
𝛼𝛼𝛼𝛼 ⋅ 𝑚𝑚𝑚𝑚(Ω) + (1 − 𝛼𝛼𝛼𝛼) 𝐴𝐴𝐴𝐴 = Ω
3 Proposed method
Recently, a new non-parametric method based on KDE has been proposed to determine BPA
[20,21]. Inspire by the idea, a score-mapping method for forensic is presented in this paper,
in which KDE and evidence theory are used to determine the confidence of whether two face
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MATEC Web of Conferences 336, 06008 (2021) https://doi.org/10.1051/matecconf/202133606008
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image pairs to compare are from the same source or from the difference source. The flowchart
of the proposed score-mapping method, named KDE-DS, is shown as figure 1.
Np pairs Compute
from the 128 dim KDE per
same vectors dim
Reference source
Database Nd pairs Automatic Compute
from the Face 128 dim KDE per
difference Recognition vectors dim
source System
BPA1
Test face BPA2 DS Output
images to 128 dim score ratio
… Combina-
compare vectors compute
Plug-in … tion rule
BPA128
Fig. 1. The flowchart of the proposed score-mapping method.
The steps are described as follows:
Step 1: First, the reference database can be divided into two parts. One is training sample
which constructs the model of each feature with its PDF curves. Another one is test sample
which is used to verify the constructed model. Each sample includes two types of images:
face-pairs from the same source and face-pairs from the difference sources.
Step 2: Automatic face recognition system is used to generate n-dimensional feature
vectors (e.g. 128-dim). For each dimensional feature, the similarity/distances of all image
pair are calculated and probability density function is generated using those
similarity/distances via KDE, which can be regarded as the probability model for the related
feature using the training sample.
Step 3: If there are new face image pairs that need to be compared (which could be regard
as evidence in a case), the automatic face recognition system is also utilized to generate n-
dimensional feature vectors, then the value of each dimensional feature would be plugged in
the corresponding PDFs. Thus, we obtain two values k1 and k2, one for the prosecution
hypothesis Hp and another for the defense hypothesis Hd, as shown in Figure 2.
Fig. 2. Generate two pdfs for each feature.
Given a hypothesis H0, the probability of H0 for each PDF model is proportional to the
specific intersection point value f(x0) [20]. According to this, a frame of discernment Ω =
�𝐻𝐻𝐻𝐻𝑝𝑝𝑝𝑝 , 𝐻𝐻𝐻𝐻𝑑𝑑𝑑𝑑 � could be constructed, and the rules about how a membership is assigned to the focal
element are as follows:
𝑘𝑘𝑘𝑘1
𝑚𝑚𝑚𝑚��𝐻𝐻𝐻𝐻𝑝𝑝𝑝𝑝 �� = 𝑐𝑐𝑐𝑐1 =
𝑘𝑘𝑘𝑘1 + 𝑘𝑘𝑘𝑘2
(6)
𝑘𝑘𝑘𝑘2
𝑚𝑚𝑚𝑚({𝐻𝐻𝐻𝐻𝑑𝑑𝑑𝑑 }) = 𝑐𝑐𝑐𝑐2 =
𝑘𝑘𝑘𝑘1 + 𝑘𝑘𝑘𝑘2
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Consider that the features of images may not be completely independent of each other,
and the source may also be unreliable, the discount efficient is considered to reflect such
effects.
𝑚𝑚𝑚𝑚��𝐻𝐻𝐻𝐻𝑝𝑝𝑝𝑝 �� = 𝛼𝛼𝛼𝛼 ∙ 𝑐𝑐𝑐𝑐1
𝑚𝑚𝑚𝑚({𝐻𝐻𝐻𝐻𝑑𝑑𝑑𝑑 }) = 𝛼𝛼𝛼𝛼 ∙ 𝑐𝑐𝑐𝑐2 (7)
𝑚𝑚𝑚𝑚��𝐻𝐻𝐻𝐻𝑝𝑝𝑝𝑝 , 𝐻𝐻𝐻𝐻𝑑𝑑𝑑𝑑 �� = 1 − 𝛼𝛼𝛼𝛼
Generally, (1-α) could be set to 0.3.
Step 4: Finally, Dempster’s combination rule is used to combine multiple BPAs to get the
final BPA, and the mass function m({Hp}) reflects the strength of evidence support of the
prosecution.
4 Experimental results
To Verify the effectiveness of the proposed method, the LFW (Labeled Faces in the Wild)
face database is used, which is a popular test set for face recognition [22]. The face images
provided are all from natural scenes in life, so the difficulty of recognition will increase. Here
we select 1100 pairs of same person and different person respectively as training set, and 500
pairs of same person and different person respectively as test set. After the aligning images
step, the available numbers of image-pairs are shown as Table 2.
Table 2. Numbers of face image pairs.
Same source Difference source
Training set 1090 1087
Test set 495 495
In forensics, transparency in the methods is essential [10]. Since Openface is an easily
available open-source toolkit, which based on the FaceNet algorithm for automatic facial
identification that was created by Google [23], it is used in the experiment, and we utilize the
commonly known matching indexes of accuracy, sensitivity and specificity, defined as:
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
𝑎𝑎𝑎𝑎𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = (8)
𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑛𝑛𝑛𝑛𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎 = (9)
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑐𝑐𝑐𝑐𝑠𝑠𝑠𝑠𝑓𝑓𝑓𝑓𝑠𝑠𝑠𝑠𝑐𝑐𝑐𝑐𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎 = (10)
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇
With N denoting the number of scores, TP the number of True Positives, TN the number
of True Negatives, FN the number of False Negatives, and FP the number of False Positives.
When calculating the accuracy, we first convert the LR values to a grade according to the
conclusion scale in Table 2. A LR value counts as a match prediction if it receives a grade
+2 or higher, and as a no-match prediction if it receives a grade +0.5 or lower. Similarly, if
the ratio of the final BPA supporting the Hp to supporting the Hd is greater than 2, which
counts as a match prediction, vice versa. The comparative experimental results are shown as
Table 3:
Table 3. Experimental results.
Accuracy Sensitivity Specificity
KDE 91.01% 90.71% 91.31%
KDE-DS 93.84% 94.14% 93.54%
From the experimental results, the proposed method has a better performance in terms of
accuracy, sensitivity and specificity than the classic KDE-based likelihood ratio method.
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After analysis, the main cause of the error is that multiple face images are extracted
incorrectly, or there is a face occluded image in the face image pair, such as wearing glasses,
so we should assure that before using automatic face recognition system for face verification,
the face image pairs to compare should be carefully checked.
5 Conclusions
The KDE method is a common method for likelihood ratio calculation in the field of face
forensics, which utilizes the similarity/distance of face image pairs to generate PDF. N-
dimensional feature vectors are integrated into a distance measure, which lose some local
information. In this study, each dimensional feature is treated as a random variable, and the
distances of each feature between image-pairs are accumulated as PDF via KDE, then the
PDFs can be mapped in BPA of the prosecution and the defense, finally the BPAs are
combined by Dempster’s combination rule. The experiments are verified that the proposed
method has a better performance than KDE method. It would be a powerful supplement to
traditional likelihood ratio calculation method.
This research was financially supported by Science and Technology Research Project of Jiangxi
Provincial Department of Education (NO. GJJ151196) and Collaborative Innovation Center for
Economics crime investigation and prevention technology, Jiangxi Province (No. JXJZXTCX-019).
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