99,530 research outputs found
Quality Classified Image Analysis with Application to Face Detection and Recognition
Motion blur, out of focus, insufficient spatial resolution, lossy compression
and many other factors can all cause an image to have poor quality. However,
image quality is a largely ignored issue in traditional pattern recognition
literature. In this paper, we use face detection and recognition as case
studies to show that image quality is an essential factor which will affect the
performances of traditional algorithms. We demonstrated that it is not the
image quality itself that is the most important, but rather the quality of the
images in the training set should have similar quality as those in the testing
set. To handle real-world application scenarios where images with different
kinds and severities of degradation can be presented to the system, we have
developed a quality classified image analysis framework to deal with images of
mixed qualities adaptively. We use deep neural networks first to classify
images based on their quality classes and then design a separate face detector
and recognizer for images in each quality class. We will present experimental
results to show that our quality classified framework can accurately classify
images based on the type and severity of image degradations and can
significantly boost the performances of state-of-the-art face detector and
recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page
Fair comparison of skin detection approaches on publicly available datasets
Skin detection is the process of discriminating skin and non-skin regions in
a digital image and it is widely used in several applications ranging from hand
gesture analysis to track body parts and face detection. Skin detection is a
challenging problem which has drawn extensive attention from the research
community, nevertheless a fair comparison among approaches is very difficult
due to the lack of a common benchmark and a unified testing protocol. In this
work, we investigate the most recent researches in this field and we propose a
fair comparison among approaches using several different datasets. The major
contributions of this work are an exhaustive literature review of skin color
detection approaches, a framework to evaluate and combine different skin
detector approaches, whose source code is made freely available for future
research, and an extensive experimental comparison among several recent methods
which have also been used to define an ensemble that works well in many
different problems. Experiments are carried out in 10 different datasets
including more than 10000 labelled images: experimental results confirm that
the best method here proposed obtains a very good performance with respect to
other stand-alone approaches, without requiring ad hoc parameter tuning. A
MATLAB version of the framework for testing and of the methods proposed in this
paper will be freely available from https://github.com/LorisNann
Forensic Face Recognition: A Survey
Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts‟ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. \ud
Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework
Terahertz Security Image Quality Assessment by No-reference Model Observers
To provide the possibility of developing objective image quality assessment
(IQA) algorithms for THz security images, we constructed the THz security image
database (THSID) including a total of 181 THz security images with the
resolution of 127*380. The main distortion types in THz security images were
first analyzed for the design of subjective evaluation criteria to acquire the
mean opinion scores. Subsequently, the existing no-reference IQA algorithms,
which were 5 opinion-aware approaches viz., NFERM, GMLF, DIIVINE, BRISQUE and
BLIINDS2, and 8 opinion-unaware approaches viz., QAC, SISBLIM, NIQE, FISBLIM,
CPBD, S3 and Fish_bb, were executed for the evaluation of the THz security
image quality. The statistical results demonstrated the superiority of Fish_bb
over the other testing IQA approaches for assessing the THz image quality with
PLCC (SROCC) values of 0.8925 (-0.8706), and with RMSE value of 0.3993. The
linear regression analysis and Bland-Altman plot further verified that the
Fish__bb could substitute for the subjective IQA. Nonetheless, for the
classification of THz security images, we tended to use S3 as a criterion for
ranking THz security image grades because of the relatively low false positive
rate in classifying bad THz image quality into acceptable category (24.69%).
Interestingly, due to the specific property of THz image, the average pixel
intensity gave the best performance than the above complicated IQA algorithms,
with the PLCC, SROCC and RMSE of 0.9001, -0.8800 and 0.3857, respectively. This
study will help the users such as researchers or security staffs to obtain the
THz security images of good quality. Currently, our research group is
attempting to make this research more comprehensive.Comment: 13 pages, 8 figures, 4 table
Directional Sensitivity of Gaze-Collinearity Features in Liveness Detection
To increase the trust in using face recognition systems, these need to be capable of differentiating between face images captured from a real person and those captured from photos or similar artifacts presented at the sensor. Methods have been published for face liveness detection by measuring the gaze of a user while the user tracks an object on the screen, which appears at pre-defined, places randomly. In this paper we explore the sensitivity of such a system to different stimulus alignments. The aim is to establish whether there is such sensitivity and if so to explore how this may be exploited for improving the design of the stimulus. The results suggest that collecting feature points along the horizontal direction is more effective than the vertical direction for liveness detection
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