Journal of Computer and Communications, 2014, 2, 70-77
Published Online January 2014 (
Low Resolution Face Recognition in Surveillance Systems
Xiang Xu, Wanquan Liu, Ling Li
Department of Computing, Curtin University, Perth, Australia.
Received October 2013
In surveillance systems, the captured facial images are often very small and different from the low-resolution
images down-sampled from high-resolution facial images. They generally lead to low performance in face recog-
nition. In this paper, we study specific scenarios of face recognition with surveillance cameras. Three important
factors that influence face recognition performance are investigated: type of cameras, distance between the ob-
ject and camera, and the resolution of the captured face images. Each factor is numerically investigated and
analyzed in this paper. Based on these observations, a new approach is proposed for face recognition in real sur-
veillance environment. For a raw video sequence captured by a surveillance camera, image pre-processing tech-
niques are employed to remove the illumination variations for the enhancement of image quality. The face im-
ages are further improved through a novel face image super-resolution method. The proposed approach is
proven to significantly improve the performance of face recognition as demonstrated by experiments.
Face Recognition; Very Low Resolution; Surveillance Camera
1. Introduction
Research in face recognition has been carried out for
more than two decades, for its potentially wide applica-
tions in commercial and law enforcement fields. Popular
face recognition approaches can achieve very high rec-
ognition performance in publicly released databases [1-6],
where the resolution of the captured facial images is
usually higher than 100 × 100. Some can even achieve
similar high performance in a very low resolution [6],
where the resolution of cropped faces is less than 10 × 10.
However, most of these works have been conducted on
databases where face images are captured in controlled
environments with high definition cameras. The so-called
“low-resolutionface images are derived from high-res-
olution faces through down-sampling and/or smoothing
methods. When face images are captured directly in a
“reallow resolution, the high performance of current
face recognition approaches is yet to be proven.
Recently, face recognition research in real-life surveil-
lance has become very popular. For high data transmission
speed and easy data storage, surveillance cameras generally
produce images in low resolution, and face images captured
directly by surveillance cameras are usually very small.
Besides, images taken by surveillance cameras are gener-
ally with noises and corruptions, due to the uncontrolled
circumstances and distances. [7] proposed a super-resolu-
tion approach to increase the recognition performance for
very low-resolution face images. They employ a minimum
mean square error estimator to learn the relationship be-
tween low and high resolution training pairs. A further dis-
criminative constraint is added to the learning approach
using the class label information. [8] proposed a matching
algorithm through using Multidimensional Scaling (MDS).
In their approach both the low and high resolution training
pairs are projected into a kernel space. Transformation rela-
tionship is then learned in the kernel space through iterative
majorization algorithm, which is used to match the low-
resolution test faces to the h igh-resolution gallery faces.
Similarly, [9] proposed the Coupled Kernel Embedding
approach, where they map the low and high resolution face
images onto different kernel spaces and then transform
them to a learned subspace for recognition.
Only a small portion of existing research is specifically
for real surveillance scenarios, where the captured face
images are very different compared with images captured
under controlled circumstances. Most of the existing re-
searches are based on the down-sampled low-resolution
face images captured by high definition cameras under
controlled environments. Even the works on surveillance
cameras [7-10] on the claimed low-resolution (lower than
32 × 32) surveillance face images are in fact on images
Low Resolution Face Recognition in Surveillance Systems
down-sampled from the original images captured in a res-
olution of 64 × 64. Face recognition based on true low-
resolution (lower than 32 × 32) face images in uncontrolled
surveillance scenarios remains an issue to be explored.
In this paper, we systematically analyze the key issues
for face recognition in surveillance scenario, where the
captured face images are usually with uncontrolled illumi-
nation, motion, poses and are generally taken in a far dis-
tance. Moreover, the off-the-shelf commercial surveillance
cameras come with low-quality sensors and can only cap-
ture images in low resolutions.
Through our analysis, we found out that three factors
which impact significantly on face recognition perfor-
mances, including the distance between the camera and the
human subject, types of cameras including sensor sizes and
quality, and the resolutions of captured face images. Three
experiments are designed to show the impact of these fac-
tors. We first demonstrate that the recognition perfor-
mances on the low-resolution face images directly captured
in real surveillance circumstances are much lower than
those on the down-sampled low-resolution images from
high-resolution images. It clearly indicates that the down-
sampled face images are not able to represent the true
low-resolution images. By changing the types of cameras
and the values of distances and resolutions, we demonstrate
that face image resolution plays a key role in face recogni-
tion although the types of cameras and capturing distances
are important factors.
Based on these observations, we propose an approach for
face recognition in real surveillance environment. In this
paper we focus on the indoor surveillance environment, e.g.,
in a corridor where people’s motions are generally walking
in a single direction in a relatively slow and steady pace.
Our focus is hence on face recognition on surveillance
captured face images with low resolutions, varied illumina-
tion conditions, small pose variation, and slow motions.
Due to the very low resolution of the captured face images,
many face features are lost. Image pre-processing ideas are
employed to remove illumination variations as much as
possible. In order to accumulate more features, we fuse a
video sequence into one frame in the frequency domain.
Curvelet features are adopted in the fusion process. The
image is further improved through image super-resolution
methods in order to increase the image resolution. Experi-
mental results demonstrate that the proposed approach is
able to improve the face recognition performance.
2. Face Image Pre-Processing
2.1. Histogram Equalization for Illumination
In real surveillance scenarios, directly captured low res-
olution images are different from those which are cap-
tured in controlled circumstances. Various factors influ-
ence the performance of face recognition, such as motion
blur, and illumination and noises in images. In this paper
we will focus on the surveillance of an ordinary indoor
environment, where a normal range of illumination con-
dition and distortion are considered without motion blur.
With a surveillance camera, video pictures are usually
captured in low resolutions. The generic commercial
surveillance cameras record pictures with resolutions
varying from 400 to 800 pixels. For example the
SWANN DVR4-1300commercial surveillance system
used in CurtinFaces database [11] captures video se-
quences with the resolution of 576 × 704. While working
in the indoor circumstance, the camera system captures
very small faces in a distance. In the SWANN DVR4-
1300commercial surveillance system, face resolutions
are around 32 × 32 in the distance of approximately 2.5
meters, 16 × 16 in the distance of 5 meters and 8×8 in the
distance of 10 meters respectively.
In an indoor corridor with no obvious side lighting, the
face images captured demonstrate quite obvious illumi-
nation effects from the natural overhead lightings during
a walking motion. A histogram equalization approach is
adopted here for reducing illumination variations. There
are generally two types of histogram equalization for
image pre-processing [12]. One is the rank normalization
where each pixel of the image is ranked and mapped to a
new image between the values of 0 and 255. Another one
is to pre-define a distribution of an image's pixels and
re-map the image into the pre-defined model. Due to the
similar feature on most part of face images, we adopt the
second method in our approach.
In detail, for a 32 × 32 grey scale face image x, the
rank for each pixel is normalized to be ri,j (ri,j
and the number of pixels is 1024 and the grey scale im-
age level is 256. A general mapping function for pixel xi,j
is defined as:
1024 0.5() ()
ij x
pfx dxFx
== =
where ti,j is the rank of pixel xi,j in the re-mapped space
with distribution function f(x) and F(x) is the cumulative
distribution function (CDF) for a given distribution f(x).
In order to remove the illumination variation, we as-
sume that the intensity distribution of face images mat-
ches the standard normal distribution. The re-mapped
face images can be derived from the inverse cumulative
distribution function. For the pixel xi,j, the re-mapped
rank ti,j is derived from:
ij ij
t Fp
where F1 is the inverse cumulative distribution func-
tion and
() /2
() ()2
Fxfx dxedx
−∞ =−∞
= =
Low Resolution Face Recognition in Surveillance Systems
The grey scale face image after histogram equalization
is derived through adjusting the pixel rank ti,jto the inter-
val [0,255].
2.2. Fusion of Video Sequence
Surveillance cameras usually capture whatever happens
in a fixed environment into a video sequence. A set of
images belong to one person with minor differences in
poses and expressions can be extracted from the video.
Illumination differences could be minimized after histo-
gram equalization as described in the last section. In or-
der to enhance the spectral features for face recognition,
image fusion method [13] is adopted here. Generally
there are two ways for image fusion. One is fusion in the
spatial domain and the other is fusion in the frequency
domain. In this paper, we utilize the Curvelet coefficients
to represent the face features [14,15].
For face recognition, Curvelet features have been
proved to perform excellently in face feature represent-
tation [16]. The proposed Curvelet based image fusion is
represented in Figure 1 which indicates that several vi-
deo frames can be fused into one image in order to derive
rich features. It is expected to generate a face image
which provides more features for face recognition. From
[16] we can see that fine coefficients represent the char-
acter of a human better. For a sequence of facial images,
we first transfer them into Curvelet Coefficients. The
smallest low-frequency components which are repre-
sented by the coarse Curvelet coefficients and the biggest
high-frequency components which are represented by the
fine Curvelet coefficients are therefore kept in the pro-
posed approach.
For the image sequence I1, I2,, In, their coefficients
are represented as Ci{j}{l}(k1, k2) (I = 1, 2, …, n). The
components of the first scale where j=1 represent the
low-frequency parts of the face image and the compo-
nents of other scales represent the high frequency parts.
The minimum components between each Ci{j} {l} (k1,
k2) (i = 1, 2, …, n) and the maximum components be-
tween each Ci{j}{l} (k1, k2) (i = 1, 2, …, n; j 1) are
kept for the fused Curvet coefficients C{j}{l}(k1, k2).
After inverse Curvelet transformation, the fused face
image can be derived as shown in Figure 1.
3. Super-Resolution Based Face Recognition
In real surveillance video sequences, face images taken
beyond certain distance always come with noticeable
noises and corruptions. When the captured face images
are below 32 × 32, corruptions are obvious. Directly ap-
plying existing face recognition approaches on them
generally will not achieve acceptable recognition per-
formances. In order to enhance the face features, we
propose a super-resolution based face recognition algo-
Inspired by [17], we make use of the sparsity of signal
representation to train low-resolution image patches pl
through a dictionary Dl and transfer the trained relation-
ship onto the corresponding high-resolution dictionary
Dh to reconstruct the high-resolution patch ph. This dic-
tionary is trained in the FRGC [18] face database inde-
pendently with both the high-resolution and low-resolu-
tion pairs. The high-resolution patch ph is reconstructed
through adopting the same coefficients in the low-reso-
lution training relationship, where a low-resolution patch
pl is represented by a low-resolution dictionary Dl with
the relationship of α. A high-resolution face image can be
derived by combining all the high-resolution patches
together. The low-resolution sparse representation is
formulated as:
where α is the sparse representation
coefficient s
inl1 norm.
This spar se representation relationsh ip is mapped to
the high dimension space. The h igh-resolution imag e
patch is derived from:
After combing the two high-resolution patches, the
hallucinated face i ma g e y c an be der ived.
Meanwhile, we adopt the idea of [19] to enhance the
same low-resolution face image into a high-resolution
one. This process utilizes the Eigen -subspace features of
human faces, which has been pr oved to have a good and
stable performance in face feature representation [2]. For
a s et of training data (FRGC [18] in this paper), the cova-
riance of zero mean face images L is: C
zero mean low-resolution face image x can be represented
by the Eigenvectors Eas:
x Ewm=×+
where w is the weight of Eigen faces and m is the mean
face. Equation (5) can be rewritten as:
()xLVw mLm
(6 )
where V is the Eigenvectors of covariance matrix
E LV= ×Λ
The hig-resolution face y can be derived from:
= ×+
where H is the corre sponding h igh-resolution training
data of L and mh is the high-resolution mea n face.
After obtaining two high-resolution faces from the
low-resolution one, a decision is made for each
pixel based on the low-resolution face image. For exam-
Low Resolution Face Recognition in Surveillance Systems
Figure 1. Image Fusion Process Diagram.
ple, for a 16
16 face image, we first enhance it into
two high-resolution images using the methods described
above. Both th es e high-resolution face images are then
combined into one i mag e w ith a pixel by pixel deci-
sion-making. For each pixel xi,j in the low-resolution im-
age, the correspond ing pixels in high-resolution is a 4 × 4
block. For a 16 × 16 low-resolution face, the reare 256
blocks in the high-resolution image. A ssume th e blocks
from the two different enhanced face images are b1and b2
respectively. In order to decide which block is to be kept,
we down-sample both the 4 × 4 blocks into one pixel and
keep the one which produces the pixel value closer to the
value of the original low-resolution pixel xi,
. The final
enhanced block image is:
arg min()
. .(1)
Down bx
stb bb
=×+− ×
( 8)
where λ equals to 0 or 1.
After combing the 256 blocks together, the final en-
hanced face image is obtained which will be used for
4. Experiments and Results
In this paper, the experiments are performed on four da-
tabases: FRGC [18], AR [20], ScFace [10] and Curtin
Faces [11]. FRGC and ARdatabases are captured with
high definition cameras. Low-resolution images are
down-sampled and smoothed from high-resolution ones.
ScFace and Curtin Faces data bases contain face images
from both high definition cameras and surveillance cam-
eras. All the face images are cropped and aligned before
being used. The high definition cameras used inAR,
ScFace and Curtin Faces databases are SONY3CCDs,
CanonEOS10D and PanasonicLumix respectively. The
surveillance cameras used in ScFace database are: Bosch
LTC0495/51, ShannyWTC-8342, ShannyMTC-L1438,
JSJCC-915D and VFD400-12B. The surveillance camera
used in Curtin Faces database is SWANNDVR4-1300 .
In real world, the reare generally two reason s for a
captured face image to be very small. One is that the dis-
tance between the camera and the person is too lar ge and
the other is that the camera sensor is limited. Although
the focal length of a camera can always be changed,
when the distance between a camera and an object is too
far away, the captured images become very small. For
simplicity, we assume that all cameras in our experiments
have fixed fo cal length.
In our experiments, the resolutions of face images are
the originally captured sizes unless specified otherwise.
None of the images are down-sampled from high resolu-
tion images. For simplicity, we divide face image resolu-
tions in to f iv e levels: 128 × 128, 64 × 64, 32 × 32, 16 ×
16 and 8 × 8. The face images are directly cropped from
the surveillance images, and if the cropped images are
not exactly the desir ed sizes, they are sligh tly changed
through Cubic interpolation to the nearest resolution level.
Four experiments are conducted. Experiment 1 compares
recognition performances between two different types of
low resolution image. One type is directly captured with
large distance between the camera and the person.
Another type is from down-sampling from high-resolu-
tion images. Results from Experiment 1 demonstrate that
the recognition performances for the directly captured
images are much low er than the low-resolution images. In
Experiment 2 the distance between the camera and the
perso n is fixed. It compares the recognition performances
between different types of cameras, resulting in different
resolutions in the captured images. In Experiment 3: the
image resolution is fixed. Recognition performances are
compared on face images from various sources, whereas
the types of camera and capturing distances vary. The
recognition performance of our proposed approach on
surveillance fac e images is demonstrated in Experiment4.
4.1. Down-Sampling vs Captured Low
Lots of work has be en done on low -resolution face rec-
ognition. Howev er, mo s t of the existing works are on
low-resolution face images down-sampled from high-
resolution images. In real life, most low-resolutions are
due to the large distances between the cameras and the
face. It is hence worthwhile to evaluate whether the
down-sampled images provide a good representation of
the true low-resolutio n ima ges . Here we compare the
recognition difference between the down-sampled im-
ages and images captured by cameras in a far distance.
Face recognition is first performed on imag es from the
popular AR database. Fig ure 2(a) shows the recog ni tion
Low Resolution Face Recognition in Surveillance Systems
rates in ter ms of different down-sampled resolutions on
AR database. In this experiment, we randomly select
13out the 26 images per person for training and the other
13 for testing. This procedure is repeated 10 times to
obtain the averag e r ecognition rate. Similarly, face rec-
ognition resu lts on the Curtin Faces High Definition da-
tabase are shown in Figure 2(b). H ere, only 25 images
are selected per pe rson from the available 92 images
among which images with large pose and illumination
variations are excluded. 12 images out of the 25 are ran-
domly selected for training and the other 13 are for test-
ing. It can be seen from these two figures that when low-
resolution face images are down-sampled from high-
resolution ones, their recognition rates do not redu ced
much. Even very low-resolution (8 × 8) faces can still
achieve a satisfactory recognition rate (around 90%).
However, when i ma g e resolution drop s due to the in-
creased distances, recognition rates decrease very sharply,
as shown in Figure 2(c). In this figure, face images are
captured using the same High Definition camera as in b.
Ins tead of down-sampling images to low-resolution, im-
ages in this figure are captured from various distances in
the same environment. The resolutions of the captured
face images are approximately in the resolution levels of
128 × 128, 64 × 64, 32 × 32, and 16 × 16 in the distances
of 2.5 meters, 5meters, 10meters and 20meters respec-
tively. In clear contrary to the various resolutions from
down-sampling, decreasing of resolutions due to the in-
creased distance from camera caused the recognition
rates drop ver y sharply as shown in Figure 2(c).
It can be concluded that the down-sampled face images
are not good representations of captured low-resolution
images for face recognition. Face recognition perfo r-
mance with directly captured images in distances through
High Definition cameras is ver y low.
To further demonstrate the difference between down-
sampling and distance sampling, databases captured
through surveillance cameras are adopted. Figu re 2(d)
and Table 1 show the face recognition rates in Curtin
Faces Surveillance Camera database and ScFace database
Figure 2. Down-sampling vs distance sampling.
Low Resolution Face Recognition in Surveillance Systems
Table 1. Face recognition performance in scface database.
Camera 1 3.08 4.62 13.85 13.85
Camera 2 5.38 4.62 18.46 14.62
Camera 3 3.85 4.62 16.42 10.00
Camera 4 1.54 7.69 20.77 12.31
Camera 5 3.08 6.15 12.31 3.08
respectively. The face images of the mare captured with
different surveillance cameras in far distances. It can be
seen that reg ard les s of different recognition approaches,
the recognition rates are also very lo w when images are
captured in distances instead of down-sampled from
high-resolution images through surveillance cameras.
4.2. High Definition Cameras vs Surveillance
It has been shown in Fig ure 2(c) that ev en images cap-
tured from a high definition camera are unable to warrant
a good recognition performance. In this experiment we
evaluate the performance of surveillance ca me r a in in-
door surveillance scenarios. The Curtin Faces Surv data-
base contains video sequen ces from a surveillance cam-
era which captures human faces in the sa me environment
as the high definition camera used above. The surveil-
lance camera is a commercial video surveillance camera
with the ima g e resolution of 704 × 576. The o r igin al res-
olutions of the croppe d face images from the surveillance
camera are approximately 32 × 32, 16 × 16 and 8 × 8
taken in the distances of 2.5meters, 5meters and 10 me-
ters respectively. Face recognition performance by the
popular LD A, LPP, PCA an d SRC methods are shown in
Figure 2(d). The recognition rates can be observed to be
similar to those of the high definition cameras with dif-
ferent distances (Figure 2(c)). However, when the dis-
tances are fixed, e.g., in 5meters, the differences of cam-
eras and resolutions lead to huge differences in reco gn i-
tion rates. In this distance the SRC recognition rate for
high definition camera is around 70% with the resolution
64 × 64, while the SRC recognition rate for surveillance
camera is around only 11% with the resolution16 × 16.
4.3. Cameras, Distance and Resolution
This experiment aims to exp lore the influences of the
types of cameras, distances and resolutions for surveil-
ance face recognition. From Experiment 1, we can see-
that when the same camera is used, images taken in dif-
ferent distances result in totally different recognition
performance. As shown in Experiment 2, when the dis-
tance is fixed, images taken by different cameras have
large differences in the recognition performance. What
would a fixed resolution lead to? We select two different
resolutions in this experiment. Figure 3(a) shows the
recognition performance for images with the resolution
of 16 × 16. Images with this resolution are captu r ed by
the high definition camera at the distance of 20 meters
and by the surveillance camera only from 5 meters away.
Figure 3(b) shows th e performance in the resolution of
32 × 32 , where HD camera is at a distan ce of 10 meters
and surveillance camera is at a distance of 5 meters. We
can see from both figures that despite the differences in
camera types and shooting distances, f ace images with
same resolutions result in similar recognition perfo r-
4.4. Face Recognition by Super Resolution
In this experiment, the proposed face recognition method
is applied and tested. Here, we carry out the experiment on
the surveillance camera. Figure 4(a) demonstrates the
recognition performance comparison between the cap-
tured faces in the distance of 5 meters by the surveillance
camera and the enhanced images by the propose d ap-
proach. In this setting, the original face resolution is 16 ×
16 and the enh an ced face resolu tion is 64 × 64. Figure
4(b) sh ow s the rec ognition performance comparison be-
tween the captur ed faces in the distance of 10 meters by
the HD camera and the enhanced faces by the proposed
approach. As shown in Figure 4, the face recognition
rates are greatly improved after the images are processed
using the proposed method, no matter which recognition
method is used.
5. Conclusion
Avoid Traditional face recognition approaches can hard-
ly achieve satisfactory performance on low-resolution
images, esp. on those directly captured by surveillance
cameras. Till now little work has been done specifically
on face recognition based on surveillance cameras. In
this paper, we analyze the factors which impact on face
recognition performances in surveillance scenario. Expe-
riments indicate that other than camera types and captur-
ing distances, image resolution is the major factor af-
fecting the performance of face recognition in surveil-
lance circumstance. Specifically we can conclude that the
higher resolution the images, the better performance face
recognition achieves.
According to the special conditions of a surveillance
system, we proposed a super-resolution based face rec-
ognition approach. Experiments demonstrate that our
approach outperforms traditional face recognition ap-
proaches significantly.
Although the proposed approach performs well for
very low resolution face recognition in surveillance sys-
tem, more practical surveillance conditions need to be-
considered, such as motion blur, extremely low resolu-
Low Resolution Face Recognition in Surveillance Systems
Figure 3. Recongnition comparision with the same resolution.
Figure 4. Recognition performance comparision between originally captured face images and proposed approach.
tion (less than 10 × 10) and face recognition in outdoor
conditions and from very far distances. They will be our
future work.
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