13 research outputs found
Recommended from our members
Vision-based spoofing face detection using polarised light
No full textComputer vision is an image understanding discipline that studies how to reconstruct, interpret and understand a 3D scene from its 2D images. One of the goals is to automate the analysis of images through the use of computer software and hardware. Meanwhile, biometrics refer to the automated authentication process that rely on measureable physical characteristics such as individual’s unique fingerprints, iris, face, palmprint, gait and voice. Amongst these biometric identification schemes, face biometric is said to be the most popular where face
authentication systems have been rapidly developed mainly for security reasons. However, the resistance of face biometric system to spoofing attack, which is an act to impersonate a valid user by placing fake face in front of the sensor to gain access, has become a critical issue. Thus, anti-spoofing technique is required to counter the attacks. Different materials have their own reflection properties. These reflection differences have been manipulated by researches for particular reasons such as in object classification. Many ways can be used to measure the reflection differences of each object. One of them is by using polarised light. Since none of the existing studies applied polarised light in face spoofing detection, therefore in this thesis, polarisation imaging technique was implemented to distinguish between genuine face and two types of spoofing attacks: printed photos and iPad displayed faces. From the investigations, several research findings can be listed. Firstly,
unpolarised visible light could not be used in a polarisation imaging system to capture polarised
images for designated purpose. Secondly, polarised light is able to differentiate between surface and subsurface reflections of real and fake faces. However, both of these reflections could not be used as one of the classification methods between real face and printed photos. Thirdly, polarised image could contribute to enhance the performance of face recognition system against spoofing attacks in which the newly proposed formula, SDOLP3F achieves higher accuracy rate. Next, near infrared (NIR) light in a polarisation imaging system do not provide significant differences between real face and the two face attacks. Apart from polarised spoofing face detection analysis, experiments to investigate the accuracy of depth data captured by three depth sensors was carried out. This investigation was
conducted due to the concerns over the stability of the depth pixels involved in 3D spoofing face reconstruction in a publicly available spoofing face database known as 3DMAD. From the analysis, none of the three depth sensors which are the Kinect for Xbox 360, Kinect for Windows version 2.0 and Asus Xtion Pro Live are suitable for 3D face reconstruction for the purpose of spoofing detection due to the potential errors made by the fluctuated pixels. As a conclusion, polarisation imaging technique has the potential to protect face biometric system from printed photos and iPad displayed attacks. Further investigations using the same polarised light approach could be carried out on other future work as proposed at the end of this thesis
Polarization Imaging for Face Spoofing Detection: Identification of Black Ethnical Group
No full textLAFAMS: Account management system for Malaysian small legal firms
No full textOne of the vital components of a successful private legal practice is good account management. Legal firms have a unique business process and specific rules on how accounting records should be kept and recorded. At present, there are many software packages for legal account management systems such as the MyCase web-based legal practice management software and the QuickBooks legal accounting software. However, for small- and medium-size legal firms in Malaysia, the software designed for international use might not be suitable. The majority of local law firms are SMEs and most of the time, their account management is done by the lawyers themselves. With limited knowledge of accounting and business management, it is not a surprise that many legal practitioners face difficulties in managing their accounts. LAFAMS (Law Firm Account Management System) was developed to assist legal firms to manage their financial transactions, monitor their performance, record cash inflow and outflow and facilitate the auditing process. The system requires only the basic Windows operating system and is easy to operate. The report produced by LAFAMS should be sufficient for submission to the Bar Council as evidence of proper account-keeping by legal firms
Face spoofing detection using surface and sub-surface reflections analysis
No full textReflection based analysis has been used in previous research for various objectives. Materials classification is one of them. Basically, each material consists of two types of reflections: surface and sub-surface. To separate these two reflections, polarized light could be applied. Previously, multireflections characteristics were analyzed using polarized light to classify objects such as between metals and non-metals. However, no trial has been done using the same method to distinguish real and fake faces that could be used to combat spoofing attempts in face biometric system. Since human skin is multi layers structure, it also produces multi reflections. In this paper, driven by the theory, surface and sub-surface reflections of both genuine human face and paper face mask were statistically examined. In addition, iPad displayed face images were also used as spoofing attempts. Images of genuine and spoofing faces were captured using polarized light under two different polarization angles: 0 and 90 degrees. Each angle captured images with surface and sub-surface reflections, accordingly. Those reflections were analyzed based on the mean, standard deviation, skewness and kurtosis. Modality distribution of each image was also studied using another method called the bimodality coefficient (BC). From the results, it is not possible to distinguish between genuine face and printed photos because of the multi reflections’ similarities. However, iPad displayed face images have been successfully identified as spoofing trials
On Evaluation of Depth Accuracy in Consumer Depth Sensors
No full textThis paper presents an experimental study of different depth sensors. The aim is to answer the
question, whether these sensors give accurate data for general depth image analysis
Face anti-spoofing countermeasure: Efficient 2D materials classification using polarization imaging
No full textFace anti-spoofing countermeasure: Efficient 2D materials classification using polarization imaging
No full textSpoofing is an act to impersonate a valid user of
any biometric systems in order to gain access. In a face biometric
system, an imposter might use some fake masks that mimic the
real user face. Existing countermeasures against spoofing adopt
face texture analysis, motion detection and surface reflection
analysis. For the purpose of face anti-spoofing analysis, skin
structure is a key factor in achieving the target of our study. Skin
consists of multiple layers structure which produces multiple
reflections: surface and subsurface reflections. In this paper, we
proposed a measure to discriminate between a genuine face and a
printed paper photo based on physical properties of the materials
which contribute to its distinctive reflection values. In order to
differentiate the reflections, polarized light (light that vibrates in
a single direction) can be used. The Stokes parameters are
applied to generate the Stokes images which are then used to
produce the final image known as Stokes degree of linear
polarization (SDOLP) image. The intensity of the SDOLP image
is investigated statistically which has shown promising results in
the materials classification, between the skin and the paper mask.
Furthermore, comparison between the experimental results from
two skin color groups, black and others show that the SDOLP
data distribution of black skin is similar to the printed paper
photo of the same skin group
Detecting Mango Fruits by Using Randomized Hough Transform and Backpropagation Neural Network
No full textDetecting mango fruits by using randomized hough transform and backpropagation neural network
No full textA new method for mango detection is presented in this paper. This method is based on preprocessing operators on image which includes converting to gray image, finding edges, calculating distances to edges, opening morphology and converting to binary color image. To take advantage of oval shaped mango fruit, we apply Randomized Hough Transform method to detect potential places for mango fruit in input images. By using Back propagation Neural Network, we recognize mango fruits from these potential places. The dataset used to implementing this paper is 50 RGB images captured of mango fruits on trees. As shown in experimental results, in the case of clear fruit in input images, the detection rates up to 96.26% while it decreases in the case of partially covering or overlapping. However, this method can be applied to detect other fruits in varied sizes and
colors
