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In the last few years 3D face recognition has become more and more popular due to reducing cost of scanners and increasing computational power. The crucial and time-consuming step is landmark localization and normalization of facial surface. Due to acquisition, noise and other artifacts like spikes and holes occur. Most systems require computational intensive preprocessing steps to eliminate these...
The interest towards biometric approach to identity verification is high, because of the need to protect everything that could have a value for some purpose. Face recognition is one of these biometric techniques, having its greater advantage in requiring a limited interaction by user. We present a Face Recognition System (FRS) based on multiple neural networks using a belief revision mechanism. Each...
Criminal activity in virtual worlds is becoming a major problem for law enforcement agencies. Forensic investigators are becoming interested in being able to accurately and automatically track people in virtual communities. In this paper a set of algorithms capable of verification and recognition of avatar faces with high degree of accuracy are described. Results of experiments aimed at within-virtual-world...
Even though facial expressions have universal meaning in communications, their appearances show a large amount of variation due to many factors, such as different image acquisition setups, different ages, genders, and cultural backgrounds etc. Collecting enough amounts of annotated samples for each target domain is impractical, this paper investigates the problem of facial expression recognition in...
Captchas are frequently used on the modern world wide web to differentiate human users from automated bots by giving tests that are easy for humans to answer but difficult or impossible for algorithms. As artificial intelligence algorithms have improved, new types of Captchas have had to be developed. Recent work has proposed a new system called Avatar Captcha, in which a user is asked to distinguish...
Many face recognition techniques have been developed during the past decades but the problem remains challenging, especially recognizing non-biological entities or avatars. Local Binary Pattern (LBP) method is one of these techniques which has shown its superiority in recognizing faces. The original LBP operator mainly thresholds pixels in a specific predetermined window based on the gray value of...
Automatic recognition of suspects from forensic sketches is of considerable interest to the law enforcement agencies. However, this task is complex due to the heterogenous nature of face sketches and photographs. To address this challenge, previous approaches generally learn a transformation of a sketch to photo or a photo to sketch at the image or feature level in order to reduce the modality gap...
Ensemble feature selection is known for its robustness and generalization of highly accurate predictive models. In this paper, we use different filter-based feature selection methods in an ensemble manner to improve face recognition. The goal is to distinguish human faces from avatar faces. Our approach was able to achieve very high accuracy, 99%, using less than 1% of the pixels in each image. This...
This paper proposes a novel hybrid hardware-software (HW/SW) system for K-means-based prototype learning and Nearest-Neighbor (1-NN) classification. We implement a prototype learning system instead of simplifying complex learning algorithms (e.g. neural and fuzzy networks, or SVMs) because this facilitates the adaptability to hardware capabilities and constraints. The K-means algorithm, which is implemented...
Monitoring of the level vigilance in humans through human computer interaction has been a field of interest for image processing researchers for long time. Numerous activities carried out by humans require constant vigil over a period of time. A lack of alertness can lead to precious human life and/or economic losses. A major cause of reduced level of vigilance is drowsiness. This paper presents a...
By transforming each image to high-dimension data set and the nonlinear dimension reduction, the 1-dimension result on the structure of the data manifold is acquired, which can be used to describe the image sufficiently. Consequently, the recognition result will be translated into the 1-dimension result. That will greatly reduce the calculative complexity and the identification error, which comes...
Dorsal hand vein recognition is an emerging biometric technique researched today. In this paper, we propose a novel approach, the local feature-based ensemble 2-directional 2-dimensional linear discriminant analysis (LFBE(2D)2LDA), for dorsal hand vein recognition. The characteristic of the approach is to combine local and global information for vein recognition. First, we use block-based (2D)2PCA...
The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup...
CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face...
Sparse representation has gained much attention of many researchers recently due to the powerful ability of representing and compressing the original sample. The sparse based classification (SRC) method has been proposed for face recognition and applied to many other fields, which method aims to sparse represent test sample on training set and minimize the reconstruction error. In order for better...
The task of successfully matching face images obtained before and after plastic surgery is a challenging problem. The degree to which a face is altered depends on the type and number of plastic surgeries performed, and it is difficult to model such variations. Existing approaches use learning based methods that are either computationally expensive or rely on a set of training images. In this work,...
We present a framework, called uniqueness-based nonmatch estimates (UNE), which demonstrates the ability to improve face recognition performance of any face matcher. The first aspect of the framework is a novel metric for measuring the uniqueness of a given individual, called the impostor-based uniqueness measure (IUM). The UNE the maps face match scores from any any face matcher into non-match probability...
In this paper, we propose a very simple face recognition method. This method first exploits a linear combination of all the training samples to express the test sample. Then it evaluates the capability of each class in expressing the test sample and assigns the test sample to the class that has the strongest capability. Using the expression result, the proposed method can classify the testing sample...
A kind of algorithm called sparsity preserving-based local fisher discriminant analysis (SPLFDA) is proposed, which insulates sparsity preserving projections and local fisher discriminant analysis in the process of dimensionality reduction. It inherits the special character of geometrical structure preserving and neighborhood preserving. Experiments operated on UMIST, Yale and YaleB face dataset show...
By combining self-training method of the semi-supervised learning with two-dimensional principal component analysis (2DPCA), a semi-supervised learning based face recognition method is proposed. On the basis of two-dimensional principal component analysis, few labeled samples are used to obtain classifier. Then unlabeled samples are classified by the classifier. And according to the self-training...
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